object detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pathlib\n",
    "\n",
    "\n",
    "if \"models\" in pathlib.Path.cwd().parts:\n",
    "  while \"models\" in pathlib.Path.cwd().parts:\n",
    "    os.chdir('..')\n",
    "elif not pathlib.Path('models').exists():\n",
    "  !git clone --depth 1 https://github.com/tensorflow/models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import six.moves.urllib as urllib\n",
    "import sys\n",
    "import tarfile\n",
    "import tensorflow as tf\n",
    "import zipfile\n",
    "import pathlib\n",
    "\n",
    "from collections import defaultdict\n",
    "from io import StringIO\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image\n",
    "from IPython.display import display\n",
    "\n",
    "from object_detection.utils import ops as utils_ops\n",
    "from object_detection.utils import label_map_util\n",
    "from object_detection.utils import visualization_utils as vis_util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# patch tf1 into `utils.ops`\n",
    "utils_ops.tf = tf.compat.v1\n",
    "\n",
    "# Patch the location of gfile\n",
    "tf.gfile = tf.io.gfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(model_name):\n",
    "  base_url = 'http://download.tensorflow.org/models/object_detection/'\n",
    "  model_file = model_name + '.tar.gz'\n",
    "  model_dir = tf.keras.utils.get_file(\n",
    "    fname=model_name, \n",
    "    origin=base_url + model_file,\n",
    "    untar=True)\n",
    "\n",
    "  model_dir = pathlib.Path(model_dir)/\"saved_model\"\n",
    "\n",
    "  model = tf.saved_model.load(str(model_dir))\n",
    "  model = model.signatures['serving_default']\n",
    "\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of the strings that is used to add correct label for each box.\n",
    "PATH_TO_LABELS = 'object_detection/data/mscoco_label_map.pbtxt'\n",
    "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n"
     ]
    }
   ],
   "source": [
    "model_name = 'ssd_mobilenet_v1_coco_2017_11_17'\n",
    "detection_model = load_model(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_inference_for_single_image(model, image):\n",
    "  image = np.asarray(image)\n",
    "  # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.\n",
    "  input_tensor = tf.convert_to_tensor(image)\n",
    "  # The model expects a batch of images, so add an axis with `tf.newaxis`.\n",
    "  input_tensor = input_tensor[tf.newaxis,...]\n",
    "\n",
    "  # Run inference\n",
    "  output_dict = model(input_tensor)\n",
    "\n",
    "  # All outputs are batches tensors.\n",
    "  # Convert to numpy arrays, and take index [0] to remove the batch dimension.\n",
    "  # We're only interested in the first num_detections.\n",
    "  num_detections = int(output_dict.pop('num_detections'))\n",
    "  output_dict = {key:value[0, :num_detections].numpy() \n",
    "                 for key,value in output_dict.items()}\n",
    "  output_dict['num_detections'] = num_detections\n",
    "\n",
    "  # detection_classes should be ints.\n",
    "  output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)\n",
    "   \n",
    "  # Handle models with masks:\n",
    "  if 'detection_masks' in output_dict:\n",
    "    # Reframe the the bbox mask to the image size.\n",
    "    detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n",
    "              output_dict['detection_masks'], output_dict['detection_boxes'],\n",
    "               image.shape[0], image.shape[1])      \n",
    "    detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,\n",
    "                                       tf.uint8)\n",
    "    output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()\n",
    "    \n",
    "  return output_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_inference(model, image_path):\n",
    "  # the array based representation of the image will be used later in order to prepare the\n",
    "  # result image with boxes and labels on it.\n",
    "  image_np = image_path\n",
    "  image_np=cv2.cvtColor(image_np,cv2.COLOR_BGR2RGB)\n",
    "  # Actual detection.\n",
    "  output_dict = run_inference_for_single_image(model, image_np)\n",
    "  # Visualization of the results of a detection.\n",
    "  vis_util.visualize_boxes_and_labels_on_image_array(\n",
    "      image_np,\n",
    "      output_dict['detection_boxes'],\n",
    "      output_dict['detection_classes'],\n",
    "      output_dict['detection_scores'],\n",
    "      category_index,\n",
    "      instance_masks=output_dict.get('detection_masks_reframed', None),\n",
    "      use_normalized_coordinates=True,\n",
    "      line_thickness=2)\n",
    "  image_np=cv2.cvtColor(image_np,cv2.COLOR_BGR2RGB)\n",
    "  person_detect(image_np,output_dict['detection_classes'],output_dict['detection_scores'],output_dict['detection_boxes'])\n",
    "  return image_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "def person_detect(image,classes,score,boxes):\n",
    "    \n",
    "    for i in range(10):\n",
    "        if(classes[i]==1 and score[i]>0.8):\n",
    "            \n",
    "            h,w=image.shape[0:2]\n",
    "            #image.shape=[height,width,3]\n",
    "            \n",
    "            ymin,xmin,ymax,xmax=boxes[i]\n",
    "\n",
    "            now = datetime.now()\n",
    "            dt_string = now.strftime(\"%d_%m_%Y_%H_%M_%S\")\n",
    "            \n",
    "            center=(int(((xmin+xmax)/2)*w),int(((ymin+ymax)/2)*h))\n",
    "            cv2.circle(image,center,10,(0,0,255),-1)\n",
    "            \n",
    "            file_name=os.path.join('C:/img',dt_string+'.jpg')\n",
    "            cv2.imwrite(file_name,image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "video=cv2.VideoCapture(0)\n",
    "\n",
    "while(True):\n",
    "    ret,img=video.read()\n",
    "    img=show_inference(detection_model,img)\n",
    "    cv2.imshow('LIVE',img)\n",
    "    k=cv2.waitKey(1)\n",
    "    \n",
    "    if(k==27):\n",
    "        break\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "attachments": {
    "output_dict.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ```output_dict``` \n",
    "\n",
    "![output_dict.png](attachment:output_dict.png)"
   ]
  },
  {
   "attachments": {
    "category%20index.png": {
     "image/png": 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8Q8nl7jFs8UY8+uijtmjRIvc6PBg77LCDHX300VuFFTKvXItHoVq1akViKvG5iWIKjwprhHC4dPl6rGsEF5U0cxGOF4Zf3NcipgYMGOCExp577lns8YTowRIxCh9CIMmJ4z88UqkaVQT5HUtVTCbM2mXTgw0KxJwX5kd47j//+U/3X2KlUPpFiCLjSFf8hN9X5i8x5DFdf+CC8EI4xlnmn9BFPK0wZUPh1FNPjXtK9TwREAEREAERKLMEStQzlS+qheKZ8jPa/ApQ5ItXlPf4jS3KM/N1T1gx5YXpIZ4QRHgPEMeIL85ao0BIYo5WWDGVHP6ajsOFF15oiAQEWllqQb4DNi7wQiFyvTBKPEaZWrow12zXLh6oF154wZ1hRw6el/eWPO+p+pbskeSafIspeabK0tejsYiACIiACOSbQInmTOVzsGFzpqL0TZ6pwiuJH2Qew4opwq8IPaOoRLKHMV2YXxjPFJ4XLzwsX5Ukg3DK1zVhPVPkYRHGh2clnWcqU9+zFVM8G48V+XAcV+B5dkqLZ0o5U/la2XqPCIiACIhAWSTgK6Zyec5UvnKmmDg/oRPH5Pq9QzlT/0s50ZOQy6IYQec0rJhinvFKJYeM4VGi2ABCKxvPFP3+4IMPXGU5cmRyGZrqMSptOVMUbyAUmcIg/I5wJhz5bqlypvzWQVAx5X2/hFZSHCOx0Qe8hFTL9MRUNjlTPDu52mGuwvzCiClPNLK+GScH/qbKQ/Njrr8XAREQAREQgbJCoETFFBWqyAGgZDAt19X8/CrtZTupfmKK52dbzS9MH/FwUPACLwctl8IlqEFKPyglTr4LBQbw2AStFBlm7GGuDSumuJ7KauToYDxTYY2qcRjTeEb22muvrMUURitFCKjqRhEGCg4gqvjzn3/+2eVLrVixwhVhoJJgtu2TTz5xOVpUxWvXrl2sRVDC9s2r5kcVO/LHevTo4VvNDzGIZ2jvvfd2Rj6lxPEaImjwEFHRjxLmJ5xwQrHuBF27XmEMnk3BEnKlvDkhv5QCMhSxqFu3btE7CAlFfFHh8bjjjiu6nsqPCECqPqaq5leoYopCG3hkKT9PsRyv+mTYOdb1IiACIiACIlBWCGQtphLPRkkFBQOCA2ZTndVT2j1TGBOdO3dOuxYwusiloCR2Yst0zhQ5OOTdYKxggGXbclXNL+q8I/DY1Sbhffr06U5UlVRLPGeKPiTO1/z5812lPYQLrW/fvq4aHwUGWLcYz4hUihB45x8heriectcY2pxRhuhJPIeMctsUqODwZcIFvecy3926dbMjjjjCfS8If96zePFiV0Kb+xAFFB445phj3BlIXBuX16rQPFOIRCrMkc/DUQHPPfdcEWfYUTI+uTGfXIeoQfCSK4kHkbL4sGWevLBMvzwr5pLiEZRI9xprHmH0r3/9y2bPnm3//ve/3ZlTlDxHiFLyPfFMKu++KOdMcW/iWkj+rSGskYqCcbQwninWI795lLVn/Zb0Rkgc49czREAEREAERCAbAhnF1I8//ujO6aEK3oEHHpjNe0r8Xi/RGwMkseJWiXesnHWAHXnCl9i9lzFWziY/4HCDFKAI+Chd5kMAAcpZetttt507AsCv8c1SZKOkN0L8+qm/FwEREAEREIF8EcgopujEG2+84Q7IZGc3rp3QfA0u8T3sDhPyxkGrCCt2/tXyT4BdcEKDMMYSD7bNf0/0xkIlIDGVn5nxvMscEE7IamJ4YqoeeFUm+b94S4MeZp6f0egtIiACIiACIlAyBHzFVMl0S28tqwQIfSP0kEINXgnpsjpWjSsaAYmpaNxyfRfii1BG8lyT87xy/W49XwREQAREQAQKlYDEVKHOjPolAuWQAAVJKHCQ2OLMDyqHSDVkERABERABERCBHBKQmMohXD1aBERABERABERABERABESg7BKQmCq7c6uRiYAIiIAIiIAIiIAIiIAI5JCAxFQO4erRIiACIiACIiACIiACIiACZZeAxFTZnVuNTAREQAREQAREQAREQAREIIcEJKZyCFePFgEREAEREAEREAEREAERKLsEJKbK7txqZCIgAiIgAiIgAiIgAiIgAjkkIDGVQ7h6tAiIgAiIgAiIgAiIgAiIQNklIDFVdudWIxMBERABERABERABERABEcghgVjE1JYtW2zevHl2++2326uvvmq77babnXvuudarVy+rXbt2Drsf/NHr1q2zoUOH2oIFC+yBBx6w+vXrp73522+/ta5du9rrr79uN9xwg1177bW+L/rpp5/cPVzbrFkz3+uTL/AOK23Xrp3NmDHDdtxxx2LPWLx4sXXu3NmWLl1qDz74oHtfSbZCn/e77rrLevfubU2bNrVHHnnEGjduXJK48vruQhz7pk2b3Pdx4403Wrp1HuQ78AOZ7bfo9/xc//38+fPtxBNPtJ9//tm96sILL7TbbrvNqlWrFvurw/7WLVu2zP3uHH/88XbVVVdZ9erVY++THigCIiACIiACpYlA1mIKg/qhhx6y6dOn2/Dhw+3ggw+2tWvX2t133+2Ey7hx46xu3bolzgSBgiEwaNAgq1q1asb+rFmzxs4//3x7/PHHberUqdajRw/f/sdhwPGMIUOG2MiRI1OKqeXLl9vZZ59tb7/9ts2dO9datGjh269cXVAa5v2ZZ56xDh06OMNv5syZBbEOczUfyc8t1LEjCi677DL3fd1xxx227bbbFkPi9x34MYzjW/R7R77+/q233nK/rbkSU1F+69avX2/Dhg2zI4880k499dR8odB7REAEREAERKAgCWQtplauXGlXXnml8+Akenv++usvu+mmm2zXXXe18847r8QHj9Br0KBBIG/On3/+aZdffrlNnDjRZs+ebW3atMlL//2MyNWrV1v37t1tyZIlTug1adIkL/1K9ZLSMO/vvvuutW3b1k477TS78847rUaNGiXGK98vLtSx4yE866yzbPDgwW7zpVKlSrGLqXyzzuX7ci2mov7WsTlFK2nveC7Z69kiIAIiIAIiEIRA1mIKo43QvquvvtoqVKiw1TsxBF577bVAYXJBOpvNNWHEFO/h+kmTJtmzzz5rhxxySDavDnyvn5j6448/7NJLL7WPP/7YeVr22GOPwM+O+8LSMO9ffPGFnXnmmdaqVSsn7KtUqRI3hoJ9XqGOnd+E5s2b26233urWcqrm9x0ULPQcdCzXYirqb53EVA4mW48UAREQAREolQSyFlOZRv3kk08aISHsRKdqhIqxU33JJZc478/48eNzFooVVkxNmzbNGXx+uTZjxoxxnjmvBcll+vrrr1344xNPPGGE7rVu3drlH5DbQ05JujA/eBFeg5BJl1eVzJlcNnLX6tSp43LFGjZsmPOF6jfvOe/A/73ghx9+cGGRhEOmyntjbV5xxRVu3eFZveCCC+yWW26x+++/3z2hZ8+eNnDgQNtll12KdZl733nnHTeHhNQlziPirWLFiu4eb30Q1kY/br75ZvfneMp4LmvnlVdesf79+xfLQSHPj3BZwuF4Pp411kq/fv2sZcuWRe9IxdNv7Mn38C5CTO+77z67/vrrXX9SeY2ynbvPP//czjjjDDfudF4NT0yNGDHCPv30Uzcnzz33nNs86NSpkxNh9erVK9aVKN8i4+ZbJ5z3vffec+8gLLRLly721FNPuXVRq1Yt9y4vn4v/fc4559iECRNsu+22c383a9Ysl89II7SUNeTdt3nzZheWyzfPxhONvErWBBsjJ5xwgguZS9WCiil+U+grHuvffvutiBVrJVN+KO8M+luX2D+JqWy/BN0vAiIgAiJQVgjkTEx9//33tnDhQmeYVK5cOSUvz9NCsjztzTffjFS8IchkIKYaNWrkPBVBGkYPxtPkyZNt++23D3KL82b5hRIibqZMmeKM9AMOOMB581atWuUMGphhSGI8pipA4Rl0hPnxriCeFq6j8AYtiNALNNAMFwWZ92zfEfR+DGWMSQzw9u3bp70Ng5U5QawioDB0N27c6HiRn4aYSV4DGNL83cUXX2yHH364bbPNNkYyPwbt6aef7sILveZ5ibgOocx8cx9t7Nixtv/++xcVLqGvNPqO4EA0ENrpCTqMZr6XnXbayQmedN9W0LEn9/Gjjz5KWxwiKPdM1xEeirhnXR500EEpL+UbIAxw3333te+++87NIUVtfv/9d1cUBoGJp5G/T9eCfIvMA4yZLwQwxRQIT/7ss89ckQwKQCRvWvCbQJ4Rgi7ZE0+BGIrwMKeekOJ5bMqwthBgO++8s+sy70bAsR5eeOGFtL97QcQU/eX3hGIrCPaaNWvahg0b3KYL3vWLLrooY35llN86iak4vgY9QwREQAREoCwQyImYwqDGsDj22GPTGnvAy5dnip1ajCZyt3IZsudnwJHzhLDBC7X77rtvtX5ggUHPDndQr1OQBZhPz1TQeQ/S73xeg8FKjhzehEQDHeMdIzXMukkV2oqYwsuCVwpR5G0i4MGgahvNK5DiedCefvpp55WhYEqyYMJAx2A/6qijYitCki/PVJB59QpIHHfccW5eksePeECg4InyREvyc/2+RTyLeODwiCOkkhuiD2E8atSord7Bn9MnRNCee+5ZdBuVCrmeAjzMq9cyzaN3D2sgqmfKbxyI0dGjRztxigCPqyHA8NqzDstTLmJc/PQcERABERCBskMgdjHF7vyXX37p8iK8UKeSxEVFwVNOOcXtzmJQpNvJj6OPfgYchjZGCAZz8q4278foxsDD65HOMxVHP3PxjEKb9zBjTJfb54kePEPJ5e4Jo8NL8eijj9qiRYvc6zCId9hhBzv66KO3CitkXrmWEE3KW6d6bqKYwqvAGiEcLp34Z11jqF933XU5CccLwy/uaxFTffr0cd9CqnL2Qfj4fYv8RuFxRCyFFQOpvFMIX8QdnilP4FHc4ZprrnHVQKMWi/HzTBH+iZeO//BIpWpUAsQLHmchHdYwa4/1T1jvP/7xj7iXgZ4nAiIgAiIgAqWCQKxiCgOF/whxKgQh5c1AoXim/EJjSmvifaHOe9AvMKyY8sL0EE8ILUK3EMcYmC+99JLLg0nM0QorppLDX9ONI5fnDwVll4vrgnwHiCU8SunOdPMTU8lzEmYcyZUs8TAhlAlb9MI0eZ4nOPBsRj3jzE9MeSGkhGdmanGH+MozFWbF6FoREAEREIGyTCA2McVBsv/5z39cvkUqr0tJQwybMxWlv34GXFn0TBX6vAeZx7BiipwlNgsoIBCkgmVYMYVxjsfkpJNOymlYahA2JXFNoXumvJBcwkDJ5yKHkVC6xFwpuOXDM4Ww492IuXSeqVzMod/GUC7eqWeKgAiIgAiIQCESiEVMcRjuV1995XaKwwipfOVMAd5P6MQxOX7vKGs5U1HnPXE3Pe4d8yjzGFZMMc94pZLDpvBEEOqF0MrGM8UYPvjgA3esAAfc5jI01eNV2nKmKAyD4KRoRKrm9y0yV+SjdezYMWXOFLlI5GVRtIYCFcntm2++cbmPhLo99thj9j//8z8pqxMiOvht5NrkeeT3b86cOa4ABTl1qQ4T9/NM8QzOwyPXL1XuV5TvIcg9YcRUoX3vQcana0RABERABEQgKIGsxdR///tfQyR4lekSX0z4F0nKlJ9OVXlu7dq1LjeCqla0XFfz86u0FxRauuv8DDjuy7aaX5g+4uGg4AVGJy1O4ZLNvFNKnCR9DFnyT9IVEQgz1myuDSumuB5DmzwVSs1T+Y/KaeSm4B3Ya6+9shZTGMkY6VTEpGQ7JbsxxvlzqsyRL7VixQpXzpxKgtm2Tz75xOVoUdwB73KcRVDC9i1f1fww8hFUHM2AEIEv+Vjz5893Zcz33ntvd35eKsHmeacQvcw/ayFVgQdEKnlTFJyhiiFVISmVTlVGjip4//33i96VipOfmOIe3kFBDPp76qmnWu3atd2mFoVKyKmioh9VVSnBHlcLI6YK7XuPi4GeIwIiIAIiIAIQyEpMEcZCZSt2RtO1THkdpd0zlXi2TKrxY9Sw60xJ7MSW6ZwpcnDIu+G8mFQ74mGXbS6q+WUz7wg8vDYU2Zg+ffpWlc/Cji3b6xPPmeJZifOFQU2VNYQLrW/fvq4aH94Dz6OASCV3xDv/CNHD9RRf4cwwNhIQPYnnkHGmFAUqKK1NuKD3XOa7W7dudsQRR7gDmRH+vIeqmJyDxX0YxuTeHHPMMXbuuee6a+PyWhWKZ8o7ywlBx/ogFwih4nGGESXh69atu9X0R/0WkwuJeOdMkecE30y5n3in+F45L4vr0zXWGedkUa0TcUQj14t72VRIFGt4ezkbjTOv0jXC+hCBiS35HeSJ4j3lSACYMa4wUQN+31ZQMVVI37vfmPT3IiACIiACIhCFQFZiKsoLS+oer1oaRkiqcJqS6ld5ey9V/zA+MYYREyXtlSpv/DXe+AgQvodnkFypXXfdNb4HF/iTEN54vMnp4xDpTE3fe4FPpronAiIgAiKQNYFyI6Y8A4CDVhFW7Pyr5Z8AO/NUPMPrkHiwbf57ojeKQHQCeA0JBdxuu+0yeqWiv6Ew78RzxmYIYYPkgaXLWfN6r++9MOdRvRIBERABEYiPQLkRU/Eh05OyIUDoG6GHFGrwM8SyeY/uFYFcEiBUl3BVQhCTD+DO5XtL27P1vZe2GVN/RUAEREAEwhKQmApLTNeLgAiUSwJePlfy4Ck8w4HglSpVKpdcNGgREAEREAERKM8EJKbK8+xr7CIgAiIgAiIgAiIgAiIgApEJSExFRqcbRUAEREAEREAEREAEREAEyjMBianyPPsauwiIgAiIgAiIgAiIgAiIQGQCElOR0elGERABERABERABERABERCB8kxAYqo8z77GLgIiIAIiIAIiIAIiIAIiEJmAxFRkdLpRBERABERABERABERABESgPBOQmCrPs6+xi4AIiIAIiIAIiIAIiIAIRCYgMRUZnW4UAREQAREQAREQAREQAREozwRiEVPr1q2zZ5991p544gl79dVXHc/TTz/drr76aqtfv35B8KWPQ4cOtQULFtgDDzyQsV/ffvutde3a1V5//XW74YYb7Nprr/Udw08//eTu4dpmzZr5Xp98gXcgaLt27WzGjBm24447FnvG4sWLrXPnzrZ06VJ78MEH3ftKshX6vN91113Wu3dva9q0qT3yyCPWuHHjksSV13cX4tg3bdrkvo8bb7zRMq3zvIKK+LKw3+KYMWPs8ccft3HjxtmRRx4Z8a26TQREQAREQAREoNAIZC2mfv31V7v00kutefPmduqpp1rt2rVt48aN9tprr9n48ePtlltusX333bfEx41AWbZsmQ0aNMiqVq2asT9r1qyx888/3xk/U6dOtR49evj2P1sxxQt4xpAhQ2zkyJEpxdTy5cvt7LPPtrffftvmzp1rLVq08O1Xri4oDfP+zDPPWIcOHez444+3mTNnWt26dXOFo+CeW6hjv+222+yyyy5z39cdd9xh2267bcGxC9KhKN/iwoULnZhi3DVr1gzyGl0jAiIgAiIgAiJQ4ASyFlOZxvfCCy/Yhx9+aIMHDy5xDMOHD7cGDRoE8ub8+eefdvnll9vEiRNt9uzZ1qZNm7z0309MrV692rp3725LlixxQq9JkyZ56VfYlxTKvL/77rvWtm1bO+200+zOO++0GjVqhB1Kqb2+UMeOh/Css85yvwl8k5UqVSqVjKN8i37fd6kEoU6LgAiIgAiIQDknIDGVZgFg6E2aNMmFLx5yyCF5WSZ+xtYff/zhvIAff/yx87TsscceeelX2JcUipj64osv7Mwzz7RWrVrZTTfdZFWqVAk7lFJ7faGO/a233nJe7FtvvdWt5dLaonyLft93aWWhfouACIiACIhAeSaQEzH1119/2QcffGA333yzjRgxIm2Y35YtW1wuyyWXXOK8P4QF5ioUK4xnigUxbdo0Z/D55dqQC3HllVcWraEguUxff/21C/chx4xwodatW9tVV13lcnvIKUkX5gevYcOGGV6HdHlVyYt53rsT2qQAACAASURBVLx51qtXL6tTp47LFWvYsGHO1nvQec9ZB5Ie/MMPP7iwSMIhU+W9rV+/3q644gq37siNu+CCC1xY6v333++e1LNnTxs4cKDtsssuxbrMve+8846bQ0LqEucR8VaxYkV3j7c+CGujH3wTNDxlPJe188orr1j//v3dGqhevXrRu8hJu/vuu11YGM/Hs8Za6devn7Vs2bLoHal4+o09+R7eRYjpfffdZ9dff73rTy68Rp9//rmdccYZbtx+OX+MgVxC1i3ikJy39u3bu3wrwlyZ07/97W9FQ0GsvPTSS/bYY4+53M1tttkmbe4m+Vvkbnnrgu+2S5cuLjyZtYBH/dxzz3UsCF1OblG+RYmpfH35eo8IiIAIiIAI5I9ArGLqyy+/dCE8FHk47rjjnMGYKV/K290lWZ725ptvRireEAQXYqpRo0bOUxGkYYxhyE2ePNm23377ILe4sCW/UELEzZQpU5yRfsABB1iFChVs1apVTrx9//33Lm8Kgz5VAQo6QZ8I8+NdQTwtXEfhDVoQoRdooEkXhZ33KO+Icg8CAeHRqVMnZ4Sna3hLmBMMZAQUBUTI+4MX+WmImeQ1MGHCBPd3F198sR1++OHOcKdwCYY4xVcIL/Sa5yXiOoQy8819tLFjx9r+++9fVLiEvtLoO4KjXr16LrTTE3QIcb6XnXbayQmeypUrpxxW0LEn9/Gjjz7KaXGIlStXOnHPujzooIPSzslnn33mWHEtBRtY6xs2bHAbCfz5nnvuaeRfVatWzT2DHD6Y8o1369bNFZjZvHmzvf/++27+4Lz77runfB8bE7/88ov79vh+O3bs6HK5ELkI2HQFI8J+ixJTUb5i3SMCIiACIiAChU0gVjHlDZV8AnaTPYMonbcpX56p3377zRmm5513Xk5D9vzEFFwQNnggkg07WGDQz5o1K7DXKcjSyqdnKui8B+l3Pq9BTJEjh0cqUfz//vvvTvSGWTc8C+9GoicMMYWHFq8UosjbRKA4xoknnuiG6hVI8e57+umn7dNPP3UFU5IFEx5AxMFRRx0VWxGSfHmmgswr64hxU6gi1WYMXCgMg3BN9EylezbfJd7CdFU2Yc9mBqJrn332CdLFSNfAmNBGPHN4GNlIURMBERABERABESjdBHIipjwkFG94+eWXnSHpV0EvVxjxkp1yyil20UUXuaT3dDv5cbzfT0xhaOPxwmBOZUhhdBNWhNcjnWcqjn7m+hmFMO9hxphKAHG/J3rwDCUb4l4I2qOPPmqLFi1yr8ODscMOO9jRRx9dTEzhxSBEE09Kqucmiik8MKwRjO50+XqsawTXddddl5NwvDD84r4W7xO5ioiloKGGbEZQrpyQTTxKhEUSnnfYYYfZzz//7LxYmcQUY/ALO4xjnHjK8N5zxAHjK6nfxTjGomeIgAiIgAiIgAiY5VRMUWIcLwz/lWSxhELxTGEwZzLaykoYUKHMe9APPKyY8sL0EE8IrZ133tmJY0QSOTsUCEn2TIURU8nhr+nGceGFF24V6hZ0vIV+Xbr5yNRvcqgQluTAHXvssS73DIFF+B5hgYTulbSYkmeq0Fee+icCIiACIiAC4QnkXEwRJkWIXS4LHwQZdticqSDPTL5Gnqn/JYKYKpR5DzKPYcUUOUsUmKCoRLKHMV2YXxgxRXGEUaNG2UknnZTTsNQgbErimrCeKTZLEFHkTKU6LiBImF+mTY64GJSVzZK4eOg5IiACIiACIlAWCORUTJE78vDDD7vd88QqZR64fOVM8T4/oRPHZPq9o6znTHkM/ebdK8hAsYNcFcUIM59hxRTzjFcq+fwxPEpU70NoZeOZou9Uw4QjeUO5DE31OBVSzhSFWBBHeLRT5Uwhnq6++mrnFaSoByKFHCvmZbfddttq6gn3o1ooIb4l7ZkKI6b4bcSjxjriMPR77rnHFR1REwEREAEREAERKCwCWYsp8mMIX6OsNPkd5IRgmD355JMuSZwS4Okq+q1du9b69OnjrqPlupqfX6W9bKfGT0zx/Gyr+YXpIx4OCl7g5aDFKVyymXdKiVN8gdArPDa1atUKM6zYrw0rpriesvmcXYXHlcp/eFPYNKhZs6bttddeWYspjGlKfC9cuNB9W4TJIqr4c3KAyJdasWKFK2dOJcFs2yeffOJytCgaQ+nxoKX3s31vuvv5TigIgQg6+OCDnUDld4U/p/gGBzH37t3bMWGdMx/8PeKTyotsXDz11FPucGu8h6VNTOHdxfNJ/ylK41V5zBVvPVcEREAEREAERCAagazFFMYdu78Y6py5QzI+hh//+FO5itLO6Vpp90xh5JBInq6RAM8BtuyeJ7ZM50yx207eDUYUJbazbbmq5hd13jF82W2nyMb06dOdqCqplnjOFH1InK/58+e7SnsIF1rfvn1dNT4KBjD2OXPmOJFKQRFvvSN6uJ5DaTkzjDPKED2J55BRHIECFXwbhAt6z2W+Kel9xBFHuAOZEf6piipw1tIxxxzjzkDi2ri8VoXkmfLWA98JBSWef/75rc6ZQmRQdS8xxBKvIN6biRMnumthA0/OjuLP2FRgfm6//XZX9jzRO5pq/SVeG9f6DOOZopgGvy2Uj2edlPSGQ1wM9BwREAEREAERKGsEshZTpQWIVy2NcCBV0Cq5WeMsJqqmUS5fRmLJzYPenF8CCGM8moQ9jx49OmXYc2KP+DYIdSzpDYf8UtLbREAEREAERKD0ESg3Yoqdd3anOWgVYcXOv1r+CWBQ4rXESEw82Db/PdEbRSB/BMilw/tI2HO6Q4C93njVHPm/eOaCHhqev9HoTSIgAiIgAiIgAh6BciOmNOWFQYDQN0IPMS5TFSUpjF6qFyJQcgTI9+rVq5fLJ23ZsmXJdURvFgEREAEREAER8CUgMeWLSBeIgAiIgAiIgAiIgAiIgAiIQHECElNaFSIgAiIgAiIgAiIgAiIgAiIQgYDEVARoukUEREAEREAEREAEREAEREAEJKa0BkRABERABERABERABERABEQgAgGJqQjQdIsIiIAIiIAIiIAIiIAIiIAISExpDYiACIiACIiACIiACIiACIhABAISUxGg6RYREAEREAEREAEREAEREAERkJjSGhABERABERABERABERABERCBCAQkpiJA0y0iIAIiIAIiIAIiIAIiIAIiELuY2rRpk9166602aNAgu+GGG+zaa68tCMrr1q2zoUOH2oIFC+yBBx6w+vXrp+3Xt99+a127drXXX3898Bh++ukndw/jbdasWegx33vvvXb++edbu3btbMaMGbbjjjsWe8bixYutc+fOtnTpUnvwwQfd+wqlFeK833XXXda7d29r2rSpPfLII9a4ceNCwZXzfhTi2FkjfB833nhjxnWeczh6QSQC8+fPtxNPPNF+/vlnd/+FF15ot912m1WrVi3S8zLdFPa3btasWTZkyBC3tk477TSrUKFC7H3SA0VABERABEQgFYHYxdQHH3xgd999tzNg+Ue3UMQUAmXZsmVO5FWtWjXjalizZo0TNo8//rhNnTrVevTo4bt6shVTvIBnYBCMHDkypZhavny5nX322fb222/b3LlzrUWLFr79ytcFhTjvzzzzjHXo0MGOP/54mzlzptWtWzdfOEr8PYU6dozvyy67zH1fd9xxh2277bYlyurdd99139KVV15Zov0obS9/6623bPr06TkTU1F+61asWGFXX321+/3cY489ShtS9VcEREAERKCUEohVTP366682cOBA69Wrl33xxRdOvBSKmBo+fLg1aNAgkDfnzz//tMsvv9wmTpxos2fPtjZt2uRlev3E1OrVq6179+62ZMkSJ/SaNGmSl375vaRQ5x1DuW3btm6n+s4777QaNWr4DaXM/H2hjh0P4VlnnWWDBw82vslKlSqVKHNEwWuvvVYwv1MlCiPEy3MtpqL81v3xxx923XXX2XnnnVeuvNAhpk2XioAIiIAI5IBAbGJqy5YtLjxt5cqVbuf54YcfLrViCs4YepMmTbJnn33WDjnkkBygL/5IPzGFsXDppZfaxx9/7DwthbD7WsjzjqA/88wzrVWrVnbTTTdZlSpV8jKPhfCSQh07Rnjz5s1dKDBruaSbxFS0Gci1mIryWycxFW0udZcIiIAIiEB2BGITU5999pmNHj3aGa277LKLE1Z+nikMcXaqL7nkEuf9GT9+fM5CscJ4pkA6bdo0Z/D55dqMGTNmqxChILlMX3/9tY0bN86eeOIJI5yldevWdtVVV7nQSDx56cL84DVs2DDD65Auryp5OcybN895CuvUqeNyxRo2bJjdikm6O8q8x9qBDA/74YcfXFgk4ZCpPKTr16+3K664wq078vsuuOACu+WWW+z+++93T+3Zs6fztLKekxv3vvPOO24OCalLnEfEW8WKFd0t3vogrI1+3Hzzze7P8ZTxXMLLXnnlFevfv79bA9WrVy96FXl+hMwSDsfz8ayxVvr162ctW7YsekcqBH5jT76HdxFiet9999n111/v+pMLr9Hnn39uZ5xxhht3cs4fvxfM13vvvWePPvqovfzyy/b0009bt27d3PzxLZITs9NOO7nv58gjj9xqGMzJc8895zZBXn31Vatdu7YL82QseHET82jIsSH/MF3jPtZBrVq1trqEb5B8HuaEvhHKzJxcdNFFdvLJJxcLIeb769ixo7vOyzFatWqVW294lw8//HC3Rg4++OCt3sP8kUfJN4swJt+vffv2LteMsER4/O1vf7O1a9danz59XDgybcqUKc4zQ0tc3/z//D4T5uw15pzfOe6FOZszhMR26dLFnnrqKdfH5PF79wYRU7BatGiRjRgxwp588kl3K+M499xzXR8zhd1G+a2TmMrXL6veIwIiIAIikEggFjHFP2IYYhgTGJK0IGLK230kWZ725ptvRireEGRKEVONGjVynoogDWMMY2by5Mm2/fbbB7nFebP8QgkxrjB4MNIPOOAAZ+BhXGHUfP/99y5vCoM+VQEKOkGfCPPjXUE8LVxH4Q1aEKEXaKD/d1HUeQ/zjmyuxVhEeHTq1MkZoukahiFzggGHgKKAyMaNGx0v8tMwnJPXwIQJE9zfXXzxxc4g3mabbYzCJRigp59+ugsv9JrnJeI6hDLzzX20sWPH2v77719UuIS+0ug7gqNevXoutNMTdAhxvhcEBSKhcuXKKYcVdOzJffzoo49yWhwCzzXinnV50EEHpew7f8c6R3Dye3L77bc7YYPQQvwiZvgu+XPP2Ge8CC1EJuLGE7OsUby45GYRXphcmCCMZ4r18dhjj9k333zjxuAJ382bNxvFGfBik7OTKIgT+SKMEXX/+c9/nLDabbfd7KuvvjLyDQlF9b5nNihYJ7wDwcifb9iwwW2i8Od77rnnVrlKjB0vH4KdNZbY6DNr+/fff3ffgjd+1iDri7UKY/r8119/Ge+GI+Iv04ZNEDGF6PNy5A477DBXqIK+UtiH+eU9mYrChP2tk5jK5tdS94qACIiACEQlEIuYYof2/fffdzvannEXREzlyzP122+/OcOB3dBchuz5iSnyABA2eCB23333lEYPO+ZBvU5BJj2Xnqmo8x6k3/m8BsOQHDkM9n333bfo1RigiN4w6yaVcY6YYncerxSiyNtEwPtBdTRa8vcC208//dR5EpIFE0YvIuyoo46KrQhJvjxTQeaV7wjBgOcK4x+meJsQVzVr1nQbDnhj+L3BGOd3BI8a3leYJjd4jRo1yk466SQ79NBDt/rrMGIKEYc3iY2jVEVsyK9krulbsmjjz1lHCGAEjCf2kvvKbwRzTqh04lr0rmNN4ElCtOOZ8hqbP3jliAxI7Bvild8+rvcqmOKxgh3RAN7mV2I/uIfrYRbVM+U3DkQb3xue9riqAVItkufttddebgMiHeMga1DXiIAIiIAIiEBQAlmLKXZpEQcYQInlxoOIqaCdzOY6SqGfcsopLgyHpPd0O/nZvMO7109MYbhh9BCik6p0LwYXRg5ej3SeqTj6GcczCn3ew4wxnUHtiR4Ms+Ry914YFuFohDLR8CLssMMOdvTRR28VVsi8ssvuGY6pnpv4veCFYI0QDpdO/LOuEVwk3OciHC8Mv7iv5TvCyPeYJ3tBksUU1TevueYa933j7UnV2FTgP4RQ4rcXRkzhZcGbnK4gDWuCeUM4I+wSW/IaSMcM7xMeLsRMmHlN551i3cEHz5U37i+//NJ5WxFLUYuy+HmmWJ98G+k86KxxvgciBeIspEOo6L/+9S/beeedXYgs4ltNBERABERABHJJIGsx5eVAUBQhVevbt6/bkfcrR57LQRaKZwqDmZbufCi/AhS5ZBT22aVh3oOOKayY8sL0EE8ILQw3DFVE0ksvveQKhCTmaIUVU8nhr+nGkctzfoKyy8V1YcVUkO8mnfEfRkwl9yt57MkiL4qYCtOf5PezUfPiiy86McfvLR4mPK5eaKB3fVBhl2lu/cSUV2jEb33EGdotz5Qfbf29CIiACIhALghkLabSdapQPFNe/8LmTEWBXZ48U6Vl3oPMY1gxRc4SIUTkqATJwQkrpjAKvbC0XIalBmFTEteEFVPyTP3vLCUeUUA4I7/BCP9ErxTX5cMzRTjiQw895MKag+R2xrHOlDMVB0U9QwREQAREICyBEhVT+cqZAoqf0AkLLtX1fu8oazlTqRgEEdFeQQaKHcRdFCPKPIYVU8wzXqnkcC+MOSqzIbSy8UwxBooScP4RuTO5DE31eBVazlSYML9sc6aef/75lGdecd4cHh5PMGebM5UY6plunVKEhiIbhE6nypnCy06RCzyiycUmeCZ5W1RBJCKANci1yUUeWKfkZVFlMFXOFDlVVDKlYA/5Xaman2eKZ5C/RahqqnFE+U797gkrpvitolIkVVT9qrb6vVt/LwIiIAIiUH4JlKiYSi7rG2fIR/KU+gmdOJZAkHdkW80vTD/xcLAzjJeDlg/hEkRMUUqcQgEYcxiY6ZLcw4w1m2vDiimux9jEWKTUPJX/yHUhp4YcDRLgsxVTXuW4hQsXupLtlK1GVPHnVFojX2rFihUuB4hKgtm2Tz75xBm+FAag/HacRVDC9i2sZ4rnIwYJZ9tvv/2cAPCKGrCBQXEKcqlSVfODIUczEA7H+Vc0ytAzfvJ+qITnlfD25oRNAIpM7Lrrru568n8QvlSpY97TVfMLIqZ4Hr8R5DSRA0bJdMQ54+PPKTxC5b/evXunFNmed2q77bZz/UY0pcq9YkODv2PsCCrWFuOgKiFl5/fee++0lQnpo5+Y4hq8YuT0kbMKWy9/ifcQJswYvbL1YddIquvDiCn6gGDlm4Vpsvcujv7oGSIgAiIgAuWDQOxiKvncJQxBShhTmji5lXbPlN9ZNZxz88ILLxTbQc50zhQ7zuTdUDUs3a5wmKWZy2p+if0IOu8IPAxOimxMnz49ZfW1MOPL5trkc3gS5wujkkp7CBdaYu4f63bOnDlOpJKngtChpDlrnesxHL3dbkQP1dS8xplSFKjAeCNc0Hsu880u+RFHHOFKeVNi3zvTiHOwuA8jHy/DMccc487q4dq4vFaF4JlKPGcKXuecc45bJ4gXmHrzg1gl75DvJPHspDDnTCWumw8//NDNJflGNApf8B0i+JOFUZhzpqgGOWDAALvnnntSLtNMHhF+I5h3vGaJ50wRWrrPPvukLGDjvQTvFFUoCbOj7H66llxExTtniqqDrK3EanjJc5PqmcnnWHGNd5YVnh9KpVPwAo8aghDxTon/uFoYMcUxBqwh7wiExOJJcfVHzxEBERABESgfBGIXU4WKzfOYsBtbksUwCpVPvvrlGTHsmiMmStorla9x6z0ikC8CHCSNQCyLlR4zMeT8PbyTeIw9r2G66wmF5Cw4NvoSz9/K1xzpPSIgAiIgAmWHQLkRU+yQEvLGQasIK3b+1fJPgPAgvDh4pRIPts1/T/RGESh7BAhrJGSR/zJ5pcrayIkSIOSVg4DxeqU6esIbM95FRBehmfwOJZ/5V9bYaDwiIAIiIAK5JVBuxFRuMerpQQkQ7kPoIWGBqXJLgj5H14mACBQnkOogbXHamgCFRSjywaHXXbp0ySi8xE4EREAEREAE/AhITPkR0t+LgAiIQIESSC7i43WT/LInn3zSWrRoUaA9V7dEQAREQAREoGwQkJgqG/OoUYiACIiACIiACIiACIiACOSZgMRUnoHrdSIgAiIgAiIgAiIgAiIgAmWDgMRU2ZhHjUIEREAEREAEREAEREAERCDPBCSm8gxcrxMBERABERABERABERABESgbBCSmysY8ahQiIAIiIAIiIAIiIAIiIAJ5JiAxlWfgep0IiIAIiIAIiIAIiIAIiEDZICAxVTbmUaMQAREQAREQAREQAREQARHIMwGJqTwD1+tEQAREQAREQAREQAREQATKBoFYxNSMGTOsW7duxYhwcOQLL7xghx9+eInTWrdunQ0dOtQWLFhgDzzwgNWvXz9tn7799lvr2rWrvf7663bDDTfYtdde69v/n376yd3Dtc2aNfO9PvmCe++9184//3xr166dwXPHHXcs9ozFixdb586dbenSpfbggw+695VkK/R5v+uuu6x3797WtGlTe+SRR6xx48YliSuv7y7EsW/atMl9HzfeeGPGdZ4tqGy/xWzfn3j/+vXr7YorrrDx48cX/fGbb74Z6Tcizn5l86ywv0Njxoyxxx9/3MaNG2dHHnlkNq/WvSIgAiIgAiJQcARiE1PLli0LJDpKigCGP30cNGiQVa1aNWM31qxZ44QNBsDUqVOtR48evt2Ow4DjGUOGDLGRI0emFFPLly+3s88+295++22bO3eutWjRwrdfubzAYxpEbOayH+me/cwzz1iHDh3s+OOPt5kzZ1rdunVLohsl8s5CHfttt91ml112mfu+7rjjDtt2221j5xPHtxh7p8zsjz/+sEsvvdS6d+9eqsVUlN+hhQsXOjHFnNesWTMXePVMERABERABESgRAuVGTA0fPtwaNGgQyJvz559/2uWXX24TJ0602bNnW5s2bfIyOX5iavXq1c4QW7JkiRN6TZo0yUu/0r2k0MXUu+++a23btrXTTjvN7rzzTqtRo0aJ8srnywt17HgIzzrrLBs8eLDxTVaqVCmfWEr0XWVFTEX5HfL7bSvRidHLRUAEREAERCALAhJTaeBh6E2aNMmeffZZO+SQQ7JAHPxWP4PDM8Y+/vhj52nZY489gj88B1cWupj64osv7Mwzz7RWrVrZTTfdZFWqVMkBhcJ8ZKGO/a233rLmzZvbrbfe6rw05amVFTEV5XfI77etPK0DjVUEREAERKBsEYhNTC1atMjq1avnBAiG3Mknn+xC1o444girUKFCSmpbtmxxuSyXXHKJ8/6QV5CrUKwwnik6O23aNGfw+eXakA9w5ZVXFo0vSC7T119/7UJennjiCSNkpnXr1nbVVVe53B5C5tKF+cFr2LBhhtchXV5VMuh58+ZZr169rE6dOi5XrGHDhrGtYPoQZd5j64DPg3744QcXFkk4ZKpQxMR8FnLjLrjgArvlllvs/vvvd0/u2bOnDRw40HbZZZdib+Led955x80hIXWJ84h4q1ixorvHWx+EtdGPm2++2f05njKey9p55ZVXrH///m4NVK9evehd5PndfffdLjSK5+NZY63069fPWrZsWfSOVBj8xp58D+/ie73vvvvs+uuvd/3Jhdfo888/tzPOOMONO1POX1C+ieMI8y2Sv0Xulrcu+G67dOlir732msuT/PDDD+3cc891LMj9TMxpJPR3zpw5dvvtt9t7773nNjU6derk5iVTLmZQMRVm7PPnz7cTTzzR/fY+9NBD9uWXX7o19sYbb9gxxxzj8rXat29fbK3wDtYuDNicobG2LrroIvfbnSkUOsrvkMRUvn719B4REAEREIF8E4hFTCE4MEb4h5sE42222cYZf6NHj3YhPRh+qZpnXJAsT8tlYjZiqlGjRs5TEaS9+uqrzoCaPHmybb/99kFucWFLfqGEiJspU6Y4I/2AAw5wQnPVqlVOvH3//feG0YFBn6oABZ2gT4T58a4gnhauo/AGLYjQCzTQ/7so6ryHeUc21yIQMHAxdDEo0zW8JcwJRiICigIiGzdudLzIT0PMJK+BCRMmuL+7+OKLXYEV1jyFSzDETz/9dBde6DXPS8R1CGXmm/toY8eOtf3337+ocAl9pdF3BAdGMqGdnqBDiPO97LTTTk7wVK5cOeWwgo49uY8fffRRTotDrFy50ol71uVBBx2Udk7C8E31kCDfoncfmwK//PKL+/b4fjt27OhyuRC5CFivaAJ/T3giGxPk/Zxzzjm28847u8d89dVXbvOFjaF999034++dX85U2LHzO4ooRMCzDslJQwAirPjfffv23Wo9/vXXX27dsQ7pL2IQ4cz/P336dPvuu+/cOk4U9skDCvs7JDGVzS+Z7hUBERABEShkArGIqXQDxMDAMEj3D3O+PFO//fabM0zPO++8nIbs+Rlw5BogbPBA7L777lthgwUG/axZswJ7nYIsrFx6pqLOe5B+5/MaxBQ5cnikEg3h33//3YneMOuGZ+HdSPSEIaZGjBjhPAaIIm8TgeIYeBVoySGTTz/9tH366aeuYEqyYPKM4aOOOiq2IiT58kxlO6+p+MYhptjMQDTvs88+abvoFbZAbOHF9LyP3g3ffPON8zji4UslRIJ6ptJ1IN3Yvefuueeebh0nrhe+/6eeemqrMNcPPvjAFdYh9DW5n/wOsRbZ5ImzWijri7BOvJJ4wNJFK2S7PnS/CIiACIiACOSbQE7FFCLm6quvdjvocYaXhYFEKfRTTjnFha+wq5xuJz/MM9Nd6yemMIbweGFopzImMLoJK0KApvNMxdHPXD+jEOY9zBj9jNRUngTC6Nidf/TRR12oIw0Pxg477GBHH310MTHFtYRoVqtWLWVVoeBKPwAAIABJREFUt0QxtWHDBnc/hme6fD3WNYLruuuuy0k4Xhh+ubg2DN84xBTP8BMPiKkBAwa4zSGES3IjdJA5RiSnmregYirs2Hku6wDRn1z+n7WNt4kqiqw9Gv97r732slNPPTXl1BFVQAgj44yzaMv777/vIhU43oFn+1VVzcW60jNFQAREQAREIG4CORVTmf6Rj3sgmZ5XKJ4pDOZMRltZCYUplHkPusbCiikvTA/xhNAi1AtxzLhfeukll4OS7JkKI6aSw1/TjePCCy/cykgOOt5Cvy4s33yKqUxHF9APNlTImUt11lwQMRVl7GHFVGLob7q1kOm8uyjrR56pKNR0jwiIgAiIQGkgkFMxRQ4JIW2Ek+y2224lyiNszlSUzsoz9b/UCmneg8xjWDFFzhIhXhSVSPYwpgvzCyOm8HCMGjXKTjrppJyGpQZhUxLXhOWbTzGVa89UlLGHFVOsRQr9pPNM5WLOy8pGUS7Y6JkiIAIiIAKlm0DWYoocn08++cTldnhhJCDxYu8plpAuFClfOVPejrFfcYhsp9JPTJWlnKls5t0ryECxg7iLYkSZw7BiinnGK5V8/hhGLRXlEFrZeKYYA3kt5F5RQCCXoaker0LKmQrLN59iilDATDlTVCQltDlV0ZognqkoYw8rpihics8997h+ZioyEeVbSndPGDHFvwsUauEbQvDRVwquqImACIiACIhAIRLIWkxhhLGL/t///teuueYaF4vPP+5PPvmkS3KmVHq6fKm1a9danz593HW0XFfzK2kxxRizreYXZhHh4aDgBfNDi1O4ZDPvlBInrwSjlF3yWrVqhRlW7NeGFVNcT+U2PK6sbSr/Ua6eXBSqvPENZCumMCgfe+wxW7hwoSt2QMU1RBV//vPPP7t8qRUrVrhiB1Rwy7axIUKO1meffZbTan5B+hmWbz7FFHmX++23n5HX1qNHj9ir+UUZe1gx5f0OUUqdXFKKbniVQdesWeN+o1544QVXjTWunKkwYoo+4PXlYHI2bbwKl0HWjq4RAREQAREQgXwTyFpM0eHNmze7XXTK7WIMUJa3W7durvBEpnOjSrtnin/oSaZO1+CAUUJJ7MSW6ZwpcnDIu8GQoMR2ti2X1fyizDsCD6FBkQ0S4xFVJdUSz5miD4nz5Z3fg3ChUV6aanwkzbNuOWcIkUpBEe+cIUQP13MoLWeGUToe0ZN4DhnltilQQWUzQrq85zLffDOcy8aBzAh/3rN48WJ3/hr3URiAAgOcH8QZSFwbl9eqkDxTYfh6BRfCfouJ3tFU64+5pAgDJdK95gkCKjNSaZEjDJ577rmi+WdOKWWf2JLPvkp+V+J883dhx55unfIsjqpg7SSvba8P/A7x988//7w7G5B1TH9YW8cee+xWkQbZfqNhxBRrnt9VSufzjZT0Zku2Y9f9IiACIiACZZtALGKqNCDyqqURjqgqUiU3Y+RTESqFyJahVHLzoDeHJxBGEIR/etm9A4HIJtvDDz/svF1+oYX8LiAES3qzpezOiEYmAiIgAiIQJ4FyI6bYeSfkjYNWEVbs/KvlnwBGFWE7GEqJB9vmvyd6owiEIyAxFY6XdzXeOTyv48aNKzoAOd2TvLwy/i9es6AHpkfrme4SAREQAREQgewJlBsxlT0qPSEOAoS+EXqIgeW3Qx3H+/QMEYiLgMRUXCTTP4ciPb169XK5tC1btsz9C/UGERABERABEciSgMRUlgB1uwiIQNknQKEUiiIkNkLWCBtWEwEREAEREAERKL8EJKbK79xr5CIgAiIgAiIgAiIgAiIgAlkQkJjKAp5uFQEREAEREAEREAEREAERKL8EJKbK79xr5CIgAiIgAiIgAiIgAiIgAlkQkJjKAp5uFQEREAEREAEREAEREAERKL8EJKbK79xr5CIgAiIgAiIgAiIgAiIgAlkQkJjKAp5uFQEREAEREAEREAEREAERKL8EJKbK79xr5CIgAiIgAiIgAiIgAiIgAlkQkJjKAp5uFQEREAEREAEREAEREAERKL8EYhdTP/30kz300EP24IMP2nvvvWd77LGHTZ482U444YQSpbxu3TobOnSoLViwwB544AGrX79+2v58++231rVrV3v99dfthhtusGuvvda374ybe7i2WbNmvtcnX+AdCtquXTubMWOG7bjjjsWesXjxYuvcubMtXbrU8eV9hdIKcd7vuusu6927tzVt2tQeeeQRa9y4caHgynk/CnHsmzZtct/HjTfeaOnWeZDvIOfwArwg7Lc4ZswYe/zxx23cuHF25JFHBniDLhEBERABERABESgNBGITU1u2bLGXXnrJJkyYYBdddJE1b97catasWTAMECjLli2zQYMGWdWqVTP2a82aNXb++ec742fq1KnWo0cP33FkK6Z4Ac8YMmSIjRw5MqWYWr58uZ199tn29ttv29y5c61Fixa+/cr1BYU8788884x16NDBjj/+eJs5c6bVrVs31zgK5vmFOvbbbrvNLrvsMvd93XHHHbbtttsWY+b3HRQC5Cjf4sKFC52YYtyF9NtYCDzVBxEQAREQAREorQRiE1MY92+++aYNGDDAqlevXnA8hg8fbg0aNAjkzfnzzz/t8ssvt4kTJ9rs2bOtTZs2eRmPnxG5evVq6969uy1ZssQJvSZNmuSlX5leUsjz/u6771rbtm3ttNNOszvvvNNq1KhR4rzy1YFCHTsewrPOOssGDx5sfJOVKlUqlWIqyrfo933na23oPSIgAiIgAiIgAvERiEVMYVjgkerfv79tv/328fUuxieFEVO8lusnTZpkzz77rB1yyCEx9iT9o/yMrT/++MMuvfRS+/jjj52nhRDKkmyFPu9ffPGFnXnmmdaqVSu76aabrEqVKiWJK6/vLtSxv/XWW85rfeutt7q1nKr5fQd5BZnmZVG+xdIwrkJgqz6IgAiIgAiIQGkiEIuYevXVV+3777+3Ll26hBo7IWLsVF9yySXO+zN+/PichWKFFVPTpk1zBp9frg25EFdeeWXRuIPkMn399dcu3OeJJ54wwoVat25tV111lcvtIackXZgfvIYNG2Z4HdLlVSVPwLx586xXr15Wp04dlyvWsGHDUHOU6eKo8x5bB3we9MMPP7iwSMIhU+W9rV+/3q644gq37siNu+CCC+yWW26x+++/3z25Z8+eNnDgQNtll12KvYl733nnHTeHhNQlziPirWLFiu4eb30Q1kY/br75ZvfneMp4LmvnlVdecRsRrIFEry55fnfffbcLC+P5eNZYK/369bOWLVsWvSMVBr+xJ9/Duwgxve++++z66693/UnlNcp27j7//HM744wz3LjT5fx5omPEiBH26aefujl57rnn3OZBp06dnAirV69eUVdmzZrlcgmT87BYn4R40i688EIjxLBatWpF9/E9kfvE/DMHv/32m+P7r3/9y95//3076aST0m6kRPkWJaayXT26XwREQAREQAQKj0AsYgoj5fDDD7fdd9/dRo0aZRg3GH4XX3yxy59KF17l7e6SLE8jTDBK8YYgWBFTjRo1cp6KIA1DjGR4imcE9bYFEWyImylTpjgj/YADDrAKFSrYqlWrDPGGIMXgwnhMVYCCftMnwvx4VxBPC9dReIMWROgFYeNdE3Xew7wjm2sRCAgPDPD27dunfRTeEuYEAxkBxRrcuHGj40V+GmImeQ3gieXvWOOs/W222cYoXIIoO/300114odc8LxHXIZSZb+6jjR071vbff/+iwiX0lUbfERyIBkI7PUGHEOd72WmnnZzgqVy5cspxBR17ch8/+uijtMUhspkL796VK1c6cc+6POigg1I+km+AMMB9993XvvvuOzeHu+22m/3++++uKAwCE08jf++1TEKF+Z0+fXoxMfXhhx+6fiC0+RZhCbcnn3zSrrnmGrdhken3KOy3KDEVxwrSM0RABERABESgsAhkLaa8Hdq///3vzvg477zznDHJrjYeFMQDO8mp8qjy5ZlixxnDlL7lMmTPT0wRFoewwQOB8ExssMCgR4gG9ToFWUq58kxlM+9B+p3PazC2yZHDI5VooGO8I3rDrBue9dprr23lCUNM4WXBK4Uo8jYRKI5x4oknuqF6BVI8D9rTTz/tvDIUTEkWTH/99ZcTYUcddVRsRUjy5ZkKMq9eMZfjjjvOzUvy+D/77DPnNcbrV6tWLffIqGIKQYQwS/x92rBhg5s/PGhx/l7AmN9CnosHjI0UNREQAREQAREQgdJNIGsx5RmGL774otvBx0BMbG+88YZhnGA8lESjFPopp5ziPGTsdqfbyY+jb35iCkMbjxeGWipDCqObECu8Huk8U3H0M45nFPq8hxljKgHE/d4Y8QwleygIo8MQf/TRR23RokXudZS83mGHHezoo48uJqa4lhBNwsxSPTdRTAUx5lnXCK7rrrsuJ+F4YfjFfS3CqE+fPu5bSFXOPhWfKGKKfpN/yO+WF+Z32GGHuWMcENC5KFhC+CAFOAhLxIvpV1k0brZ6ngiIgAiIgAiIQLwEshZT3tkx//3vf1NWTMMzguGXLtk83uGkflqheKYwmGl+uSLpcqbywSroO0rDvAcdS1gx5YXpIZ4QWjvvvLMTx4gkjgfAQE/M0UIkhxFTyeGv6caRKg8o6JgL+bog4XBsXJCb5oncqGIqmcPmzZtt/vz57qw8Qv3iLKcvz1Qhrzr1TQREQAREQASiEchaTPFadshJxMejkrybi6H6ySefuATwkmxhc6ai9LU8eaZKy7wHmcewYoqcJQpMUFQi2cOYLswvjJhCqJJ7mKkAQpBxldZr8umZSseIjQ9yGOPcBAoiEkvrnKnfIiACIiACIlBeCcQipn799Vd3ECeJ5STZe83LieJ8p8Q/T/77QqzmF2VB+ImpspQzBZ+o8+4VZKDYQdxFMaLMW1gxxTzjlUo+fwyPEnk8CK1sPFOM4YMPPnC5V3xXuQxN9XiVtpwpCsMgOL1cJ69oBV7dZG8S3nGOEkis5sdZcoRdnnvuuSlDCb1KmyUlpvjtZCyso1NPPdXuueceV3RETQREQAREQAREoLAIxCKmGBIGKUnhJHNTfpvQOg6WJYme5PvEksQegrVr17rciKlTp7o/ynU1v6CH9kadIj8xxXOzreYXpm94OCh4gdFJy4VwiTLvlBInt65jx44u/M0rIhBmbHFeG1ZMJY+Zyn8UW8FYr1mzpu21115ZiymM6ccee8wWLlzoSrZTFhxRxZ///PPPzhu8YsUKV86cSoLZNrzHFEaguENyifFsnx32/ijV/DxvHps25GfCCnFLZT6ON9h11123ElNeKCXVESkyQtl6rzomLAjxo2DI3nvvHbb7aa8P45las2aN83zyG0pRGq/KY2yd0YNEQAREQAREQARiIRCbmKI3lBrGcKcYRe3atX1Lo+ermh99CyJ0whL1zrdJdx8MXnjhhWJeuUznTJGDQ94NRhRVEbNtuarml9ivMPPu5VoREkq56uSCJdmON8z9iedMcV/ifJE3Q6U9hAutb9++zrimYADrds6cOW6tU1DEO/8I0cP1HErLmWEY8YiexHPIKHRAgQo8HoQLes9lvrt162ZHHHGE86Ig/JPPQeKsKQoyHHPMMc6jwrVxea0KxTOFuEZEIOhYH3gv2aDxOMOIkvCpcpkoCsLmAUVBaKwtrmXThjlJnF88U/wmUJyGs6YQXYhkSrBTRn/AgAFbnWUVZl2luzaMmKJPFKmgfDzrpKQ3HOIYv54hAiIgAiIgAmWRQKxiqpABedXSKDWtClolN1OcxUQBDoxhGYklNw96c34JIIwRaw8//LCNHj065VERiT3i2+BA6ZLecMgvJb1NBERABERABEofgXIjpth5Z9eag1YRVuz8q+WfAAYlIUsYiYkH2+a/J3qjCOSPALl0eB/HjRvn8u0yNS8Ekf87fvz4wIeG5280epMIiIAIiIAIiIBHoNyIKU15YRAg9I3QQ4zLVAc5F0Yv1QsRKDkCFKqhmA/5pC1btiy5jujNIiACIiACIiACvgQkpnwR6QIREAEREAEREAEREAEREAERKE5AYkqrQgREQAREQAREQAREQAREQAQiEJCYigBNt4iACIiACIiACIiACIiACIiAxJTWgAiIgAiIgAiIgAiIgAiIgAhEICAxFQGabhEBERABERABERABERABERABiSmtAREQAREQAREQAREQAREQARGIQEBiKgI03SICIiACIiACIiACIiACIiACElNaAyIgAiIgAiIgAiIgAiIgAiIQgYDEVARoukUEREAEREAEREAEREAEREAEJKa0BkRABERABERABERABERABEQgAgGJqQjQdIsIiIAIiIAIiIAIiIAIiIAISExpDYiACIiACIiACIiACIiACIhABAISUxGg6RYREAEREAEREAEREAEREAERkJjSGhABERABERABERABERABERCBCAQkpiJA0y0iIAIiIAIiIAIiIAIiIAIiIDGlNSACIiACIiACIiACIiACIiACEQhITEWApltEQAREQAREQAREQAREQAREQGJKa0AEREAEREAEREAEREAEREAEIhCQmIoATbeIgAiIgAiIgAiIgAiIgAiIQKxiat68edarVy+rU6eO3Xvvvda4cWMRFgEREAEREAEREAEREAEREIEySSA2MbVp0yYbOnSojRo1yoEaPXq0DRo0qExC06BEQAREQAREQAREQAREQAREIDYxBUp5prSgREAEREAEREAEREAEREAEyguBWMVUeYGmcYqACIiACIiACIiACIiACIiAxJTWgAiIgAiIgAiIgAiIgAiIgAhEICAxFQGabhEBERABERABERABERABERABiSmtAREQAREQAREQAREQAREQARGIQEBiKgI03SICIiACIiACIiACIiACIiACElNaAyIgAiIgAiIgAiIgAiIgAiIQgYDEVARoukUEREAEREAEREAEREAEREAEJKa0BkRABERABERABERABERABEQgAgGJqQjQdIsIiIAIiIAIiIAIiIAIiIAISExpDYiACIiACIiACIiACIiACIhABAISUxGg6RYREAEREAEREAEREAEREAERkJjSGhABERABERABERABERABERCBCAQkpiJA0y0iIAIiIAIiIAIiIAIiIAIiIDGlNSACIiACIiACIiACIiACIiACEQhITEWApltEQAREQAREQAREQAREQAREQGJKa0AEREAEREAEREAEREAEREAEIhCQmIoATbeIgAiIgAiIgAiIgAiIgAiIgMSU1oAIiIAIiIAIiIAIiIAIiIAIRCAgMRUBmm4RAREQAREQAREQAREQAREQAYkprQEREAEREAEREAEREAEREAERiEBAYioCNN0iAiIgAiIgAiIgAiIgAiIgAhJTWgMiIAIiIAIiIAIiIAIiIAIiEIGAxFQEaLpFBERABERABERABERABERABCSmtAZEQAREQAREQAREQAREQAREIAKBWMXUvHnzrFevXlanTh279957rXHjxhG6pFtEQAREQAREQAREQAREQAREoPAJxCamNm3aZEOHDrVRo0a5UY8ePdoGDRpU+ATUQxEQAREQAREQAREQAREQARGIQCA2McW75ZmKMAO6RQREQAREQAREQAREQAREoFQSiFVMlUoC6rQIiIAIiIAIiIAIiIAIiIAIRCAgMRUBmm4RAREQAREQAREQAREQAREQAYkprQEREAEREAEREAEREAEREAERiEBAYioCNN0iAiIgAiIgAiIgAiIgAiIgAhJTWgMiIAIiIAIiIAIiIAIiIAIiEIGAxFQEaLpFBERABERABERABERABERABCSmtAZEQAREQAREQAREQAREQAREIAIBiakI0HSLCIiACIiACIiACIiACIiACEhMaQ2IgAiIgAiIgAiIgAiIgAiIQAQCElMRoOkWERABERABERABERABERABEZCY0hoQAREQAREQAREQAREQAREQgQgEJKYiQNMtIiACIiACIiACIiACIiACIiAxpTUgAiIgAiIgAiIgAiIgAiIgAhEISExFgKZbREAEREAEREAEREAEREAEREBiSmtABERABERABERABERABERABCIQkJiKAE23iIAIiIAIiIAIiIAIiIAIiIDElNaACIiACIiACIiACIiACIiACEQgIDEVAZpuEQEREAEREAEREAEREAEREAGJKa0BERABERABERABERABERABEYhAQGIqAjTdIgIiIAIiIAIiIAIiIAIiIAISU1oDIiACIiACIiACIiACIiACIhCBgMRUBGi6RQREQAREQAREQAREQAREQAQkprQGREAEREAEREAEREAEREAERCACAYmpCNB0iwiIgAiIgAiIgAiIgAiIgAjEKqbmzZtnvXr1sjp16ti9995rjRs3FmEREAEREAEREAEREAEREAERKJMEYhNTmzZtsqFDh9qoUaMcqNGjR9ugQYPKJDQNSgREQAREQAREQAREQAREQARiE1OglGdKC0oEREAEREAEREAEREAERKC8EIhVTJUXaBqnCIiACIiACIiACIiACIiACEhMaQ2IgAiIgAiIgAiIgAiIgAiIQAQCElMRoOkWERABERABERABERABERABEZCY0hoQAREQAREQAREQAREQAREQgQgEJKYiQNMtIiACIiACIiACIiACIiACIiAxpTUgAiIgAiIgAiIgAiIgAiIgAhEISExFgKZbREAEREAEREAEREAEREAEREBiSmtABERABERABERABERABERABCIQkJiKAE23iIAIiIAIiIAIiIAIiIAIiIDElNaACIiACIiACIiACIiACIiACEQgIDEVAZpuEQEREAEREAEREAEREAEREAGJKa0BERABERABERABERABERABEYhAIBYxtWXLFlu0aJHddNNN9uKLL1rt2rXt4osvtosuushq1KiRslvDhw+3oUOHFvu7I444wmbOnGkNGjSIMJx4b3njjTfskksusZ49e9qll16a8eF33XWX9e7d25o2bWqPPPKINW7c2LczX3zxhfXr18/GjRsX6PrEB3733Xd29tln25w5c+zBBx+0rl27pnxflH75djyLCzZv3myvvfaaWyuvvvqqHXjggTZgwADX/6pVqxZ7MozOPPNM++ijj4r93ejRo23QoEFZ9Cb7WxcvXmydO3e2pUuXZpyH7N9UeE8o1LHncs3Pnz/fTjzxRPv555/dhFx44YV22223WbVq1Upsgrw+1atXL/Bvj9fZb7/91n17r7/+ut1www127bXXZhzHsmXL3PXHH3+8XXXVVVa9evUSG7deLAIiIAIiIAKFQCAWMTV37lx7/PHH7eqrr7Zdd93V/vrrL2cw8x//OKf6BxcxhWBKJwJKGs6PP/7ohM7gwYOdwe/XnnnmGevQoYMzMhCDdevW9bvFshFT3sNnzJjh/mc6jlH65dvxiBcguh966CEnPBCn22+/vf3xxx82a9Ys++GHH6xv375WuXLlrZ4Oo+uvv94mTJhgO+64Y8Q35+625cuXO1H79ttvG99BixYtcveyAntyoY49X2v+rbfesunTp5e4mGJZ8B1dd911dt5554XamFmzZo2df/757vd76tSp1qNHD99Vtn79ehs2bJgdeeSRduqpp/perwtEQAREQAREoCwTyFpMrVu3zokoPFGJ3hgM59tvv92aNGlirVu3Lsaw0MVUWCP+3XfftbZt29ppp51md955Z1qPXNyLyU9MlVS/Uo1z5cqVThiNHDnS6tSpU3SJZ5whCPfff/9SJaZWr15t3bt3tyVLljiDlPVeXlqhjj1fa74siKk///zTLr/8cps4caLNnj3b2rRpE2j5+v3uBHqILhIBERABERCBMkAgazH1008/2ZAhQ5yBnOw54B/cb775JmUoVlkTU144WqtWrVwIW5UqVfKyPPyMmpLqV6rB05d7773X7Wonh0WxHvbbbz87/fTTS5WYwiOAl+3jjz92Hsk99tgjL/NeCC8p1LHna82XBTHFOuLbmzRpkj377LN2yCGHBFpafr87gR6ii0RABERABESgDBDIWkzhmSL3qU+fPrbnnntuheSee+5xoXzpPFMY1L/88ovLNSEHoWPHjnbNNdfY3nvvnRbtqlWrXDgY+TaTJ0+2f/7znzmZhrCeKcLUCPcizCtT3gFemCuuuMLGjx9f1O8333zTmjVrlnYc3PPcc885g4dxY7B369bN+vfv73aTaenC/IL2y3s584k4vu+++5wXiXdUqlQpFsZ4phCaPLdWrVpFz9y0aZONGjXKOnXqlNIzBU/WBkIsSJ5VLJ0N+BA8sIhDvCEYmKlCEQljJK+qXbt2jit5hXhtEWAnn3yy402uYIUKFbZ6K/P+zjvv2BNPPGGErhFWx7dErgqivWLFiu567/mEmPIdjh071lasWGFjxoyxY4891n1TDz/8sPtW4J8Ygso7eP6NN97o+kPjHeQ70rdUeWxeJ4OMPXFAXE8+IXmIeED4BoKEwwaciqLLwqx51vu0adNciNt7773nvi04dunSxZ566imXR5S4VhP7ElRMpXoHa50w4vr166ccHmNgvT/wwAMuHBivf/v27d0aIpyUb+Jvf/tb0b3JYX7kNvF7xJhomfILGf+tt94aKt9KYirsqtT1IiACIiACZZVA1mIKMPxjj1FP7L3nceAfc4ojnHPOOcXyYLgH4+3zzz+3gQMH2r777usMw08++cT9o37llVe6P0vVMGCaN2/u/iqXyd+MacSIEa44RDpjKrF/GEwYRxhJGD1BmrezT5hYOjHliVUEJsYReUYbNmxwxjuGOcboQQcdlFZMhe1XYsEHDLd0AiHI+FJdQ1EPDH2KSnj5URh8FNQg/yJZUHz55ZeusAdikRBK5uLXX391uSqM7bLLLku5vqL2L8p9GL2E+bHDn84jiQeXtV6zZk236eB9KxQPINfl5ptvLhYiSJ4YuViE0B5QbBmwAAAgAElEQVR++OG2zTbbGAUDMPDx4BFW6jWeDyOYsmYRQaxH8tP41hAvFGbYuHGj86TBmdxGhBcbFAgchATCmf8fvswJ78pUZCDI2L0+euudftD8NhGizAX3BF3zjJPfGlgiThknTD777DPHjA2eTOs/iJjiHYha5uYf//hH0fpmDSMm+S1r2bLlVkPl/Xj6e/Xq5fKSWFPeN8+fs36Si14kiylE8pQpU5yniWdkamxQMI9sTvH7EqRJTAWhpGtEQAREQATKA4FYxBQGCDvfeJjWrl1rDRs2dEYduVRhq/JhXL700kvu3lQekXx4pthBx8B4+eWXMxrI2S6QIGIKrwGGMsZ3stAghBIhRtJ5XIU8cumZghdjJqeMOcbgY63gzUFo7LzzzoGRZsqzCvyQPF7IHOJNZL7weiTOJZ4B/t6vYqTXXYx4r7hLopjCK4WX2NuIQNyRm4YY5X3Jxv8HH3zgPDJ4q5IFE98ABjP3xbW28uWZCjKtrB88pIhMhFRyw4uKkMRjGtUzxe8i95900kl26KGHFnsHgop3MG+eh4o8NCpUskmQakPp008/dXPGfek8U2ywIJLZ2AkatheEWeI1/D7iZeQ96Sq2hn2mrhcBERABERCB0kggazGFgYSHZLfddrMTTjihyEgkfA9jjpC8dKEsqYCx846RQ1hKEI9Q3NAxshByeNUwcg844IC4X1H0PD8xxd8TzoOQ2meffYr1A/beTnVcBm/OBmvmdv2ZV0pLH3zwwUWvwlOFRxIPTZg5x8Oxww47OC9XoTe/3EI8ucnhoV6o16OPPuqOHqDhZWDMRx999FbXp3p+cl5ispjCu7HXXnulrchGWCHhiBjuZc1gxuN5xx13OLETdWx+nqkg/PiNYdPI+37xOJO7BPMw4bWeZ+qoo45yYZ+Ejqbz7sfxrXjvY20++eSTzuumJgIiIAIiIALlkUDWYooS4uRkYLgl5z+ws42ninC8oC2T0Rn0GdleVyieqSAsSlO4DSGEd999t1sr2267bdE0eaKQvKGg1cS4uTSNPayYwgNLeB/iCW8WXju8RBixePXIb0oUX1HEVLqz3hK/n1yEemb7fcZxf6ZiKEGf7yemgryDNZwopFN5HYP0x9uYwVvN5hU5esnhg0GeE/QaeaaCktJ1IiACIiACZZ1A1mIKg4HYf3IMknd4oxgGHARKGNgtt9wSecc4jkkLmzMV5Z3lzTPFenj++eedmEredQ8rjChagZg47rjjQgmwKPMUxz1hxRReN/IIU4V3pgvzS66q6eeZIk+GDZDyeFZQWfRMeV5sDu/Fu3/KKafk7NyzsN9rHN+QniECIiACIiAChUggazHFoY8kvA8YMKCY+CHn6LfffnOx+4kNoUQ4V3JxCsLAyN/g4F/ygFK1fORM8d6w1fyiTK6fmOKZZSlnirAnwoLIB0kWUxj2HI5MoQWvsWNPuBPV8pJLjpOkj8FIGBrrJbnhMcWjQ+U8QrnIQwkTNhVlPjPdE1ZMIYTwSiV76lgzFDRAaGXrmfr666+NipuEtWYqMhEXi0LKmYIja4Iqkalypgj3JfS0UaNGxcr1ezz8PFNRcqa+//57V+2Tio2pwvT4PWW+WNuJ30pyAQpCRCmgQ75cLsL9woipxKI25NWWhpDkuNa8niMCIiACIlD2CWQtpkA0b948l9PRo0cPJ5IwIvj/id2nWlZyHgz/0FNwACOYnCryrcixwqBesGCBE2fpSiYTXkLpYlquq/lhrFNRLVW56ziWRhAxlW01v7D9pKLiGWec4SqaxR3ihTHNwbYYqhixGPBUKaOYAsUQMCITS3FzPfkYrAcMSMp8IyJYW8zNueeem7Y0PuuInfn/+Z//sYceeqhY2f6wXLK9PqyYwlDHmGdzgSIdVOEjn4Y8JyoCkuuUrZjyvl34UAqdvDyvGiGbJHzXL7zwgstzi5pXlMiNkF+KLVBAgZaran5B5wojH0FFJUMEFUVzWI8UwWHNUUEzk9D0E1P0g80fcgGpGIgXlXewrskN5aBcKjImh+PBnXyuwYMHu9xC1jy/A/w5BR+oaolI8qph8p5kMeW9m5xKfmMzHTcRlFfidWHEFGX9O3ToUHS8QZi8yCh90z0iIAIiIAIikE8CsYgpjAPKW2N0IXZq167tyvryDz7/O1XzzrchnIlzUzhHBYOOsKZMu+Sl3TOFVwGBma6RN8Thr4lVEP3OmaJKHMbK/fffH6qAQ6o+5Lqa3+bNm12oH8aid2YUuUF4KZMP8vX6hweFMFJyhShXTaETdu4TS00nj4V1xfrDc0WJ8ORKiPn8yLxzoLx3emf+ULmtZ8+e9vTTT7u/Spx7vimKoOBV884Ww8N7wQUXOAaU1G7atKmrqIYAJkfGa5TExrPr5UR5a+P//b//5+7jm0QkeZ4N+BKqy7wgMPAC0hfEKgI23byEZVhInimv78lFPrxzpuAHA+8sL65PPrsp1fhTnecU5Zyp5DnxzpnyitEkrmfEH0VdCO9jPXAtLXHdJc952LlLvj6omPLCcdmUotw+a1FNBERABERABMoSgVjEVFkC4o2FMDGMcHaHCT9TKz0ECIXC24DxS1hRmGqSpWeU6qkIlAwBz1tOyfdUB7In9goPHGF9RBqwwSGvVMnMmd4qAiIgAiKQOwISUxnYcsAsRjmeH8KB1EoHAS9Hg7lLVcChdIxCvRSBwiPABgXiiFBrvMN+uXaEQuJRxSuVeMh04Y1MPRIBERABERCBaAQkpqJx010FTIDcIsIpJ02aFOog4AIekromAqWSAGGH5HrxPfoJr1I5QHVaBERABESg3BOQmCr3S0AAREAEREAEREAEREAEREAEohCQmIpCTfeIgAiIgAiIgAiIgAiIgAiUewISU+V+CQiACIiACIiACIiACIiACIhAFAISU1Go6R4REAEREAEREAEREAEREIFyT0BiqtwvAQEQAREQAREQAREQAREQARGIQkBiKgo13SMCIiACIiACIiACIiACIlDuCUhMlfslIAAiIAIiIAIiIAIiIAIiIAJRCEhMRaGme0RABERABERABERABERABMo9AYmpcr8EBEAEREAEREAEREAEREAERCAKgVjE1JYtW2zRokV200032Ysvvmi1a9e2iy++2C666CKrUaOGb79WrFhh06ZNswcffNC++OILO/DAA93/btKkie+9ubzgjTfesEsuucR69uxpl156acZX3XXXXda7d29r2rSpPfLII9a4cWPfrjHWfv362bhx4wJdn/jA7777zs4++2ybM2eOY9W1a9eU74vSL9+OZ3HB5s2b7bXXXnNr5dVXX3VzPWDAANf/qlWrZnzy+vXr3XgZE/fSuPeaa66xKlWqZNGr6LcuXrzYOnfubEuXLs04D9HfULh3FurYC23N53MGw4x99erV7rdtxx13dN9j3bp189lVvUsEREAEREAEygSBWMTU3Llz7fHHH7err77adt11V/vrr7+cwcx/1157rVWvXj0lLK6bMWOG/fvf/3ai5bDDDrNq1aoVBNgff/zRCZ3Bgwc7g9+vPfPMM9ahQwc7/vjjbebMmYEMk2zElNcf+NHSiako/fIba9S/R3Q/9NBDTnggTrfffnv7448/bNasWfbDDz9Y3759rXLlyikfv3z5crvqqqvs0EMPtS5dutguu+xiFStWjNqV2O6jX4jat99+2/gOWrRoEduzC/1BhTr2Qlrz+Z7DsGNnc+Oee+6x33//3XfDKN9j0ftEQAREQAREoDQQyFpMrVu3zokoPFGJ3hgM59tvv915l1q3bl2MBX//8MMPO2M6iFci3zAROtdff71NmDDB7dz6tXfffdfatm1rp512mt15552BPHJ+zwzy935iqqT6larvK1eudExHjhxpderUKboEj9OwYcPcOth///2L3bpq1SobPXq09enTxxo0aBAES96uYXe/e/futmTJErehUNLe1LwN3MwKdeyFtObzOR+8K8rY33rrraKNr3z3V+8TAREQAREQgdJOIGsx9dNPP9mQIUOcgZwsOjD0v/nmGxs0aFAxTl999ZUzPvFQpPNGlCTcsGKK688880xr1aqVC5nJV9iZn5gqqX6lmjv6cu+99zrhlOyBHD58uO233352+umnb3UronvSpEnWvHnzghQqbAawhj/++GPnkdxjjz1Kctnm9d2FOvZCWvN5nRAzFyYd9ndIYirfs6T3iYAIiIAIlCUCWYspPFNDhw51XoM999xzKzaEj+BJSOWZuv/++61Ro0bWrFmzUDzxUhAORs7M5MmT7Z///Geo+4NeHFZMEaZGuBdhXoQ2pmt4Ya644gobP3580SVvvvlmRg7c89xzzzlRwbgx2Lt162b9+/e32bNnu+ekC/ML2i+vM8wn4vi+++5zXiTeUalSpaDYMl6HZwqhyXNr1apVdO2mTZts1KhR1qlTp2KeKe65++67nSAvlBDQxEEi9hCHeAQQtqm8mIQxklfVrl07x5W8Qry2CLCTTz7Z8T7iiCOsQoUKW/Fj3t955x174oknjPAtwur4lgh3RLR7YY7e8wkx5TscO3askYc4ZswYO/bYY11OGV5gvpXk3BjewfNvvPFG1x8a7yDfkb5lymMLMvZkVuQTEtLbpk0b9w3kIk/Hb80vW7bMfavvvfdeUffY2EHIeyy9v2BeEMmeRzTonCSOm/6wifDoo4+63NKWLVu69cB3TF/4xhIb3yBr/o477nBzTt4pc0LYMfdmCm/1G3uqD1RiKpafNz1EBERABESgnBLIWkzBDeGBUX/++ecXGbwYCRQLOOecc4p5njZs2OA8WRg0GA5e4Yr69ev7FiPgH368FLQLL7zQbrvttpwY2YxpxIgRrjhEouGfbp0wDowdBEH79u0DLSdvZ58wsXSi0hOre++9t+NFnhH8MN4xzDFGDzrooLRiKmy/vJ3tjz76yBn/6QRCoAGmuIiiHhj67J57HkmMWgpqnHrqqcUExYIFC9zagu2UKVOcoPz222+tY8eOTiTApaQbhjJhfnjX0nkk8eAOHDjQatas6TYdvG9l/vz5dt1119nNN99czPNGiCm5WITQHn744bbNNtu4sd9www3O8Ces1Gs8H0ENU9YsIghm5KchlBAvFCfYuHGj86Qh3MhZRHixQYHAwbhHOPP/T58+3c0J70qX88i7g4zd66O33ukHzW8TIeq8Bl3ziMgPP/zQ8U/cMEDcI5D5rvieE1uYOeG+X3/91c0DwvTEE090LOGOqGL98luWuPlC36+88kqrV6+eCx8lN5D29ddfu/nbaaednPhK580POvbEMUlMRV1puk8EREAEREAEzGIRUxgH7HxTVW7t2rXWsGFD9489uVT/X3vnAWVFsa3hMiMmBL0IKgZ0AYKiYgDMWVEEDJgRLoJZEQyYc0ARs5gRE17jQ0VEDAhmMSHB8IzvmhVFDJjf+uq+mtfT0306njN9Zv5ai3Wvc7qrq/7a1b3/nSoox4VkZxQIPFNU8UOZwdKO1ZeiFVhjqYwXpDBUwjOFxR0P0OTJk0sqyFkFKA6ZQuFDUUb59nsuCKFE4Ro4cGAomUo6xnJ6phgLcyanbNKkSXa9kRW8ORCNli1b1hnu1KlTrVeO+5o0aWKGDh1qr0O5RN7wtnTo0CHpNCt+PWuIN5H1ooCGdy3ZA/weVTHSDTpI+eV+vFJ4iR0ekDty09hLPI/7IEnOADF9+nQzduxYa8zwEyb2AESa+8K8nklBpM9KeKbijguv57Bhw6xhx+tVJwQZbyFEk4I6cVoYIYFM4Ymm6qRfTvE2vv/++7XWffz48WbWrFnWE+t//zny261bt1wLnfA8DEcQ+lVWWSXOdHWNEBACQkAICAEh8H8IZCZTKEh4SFq3bm123nnnGiXx+++/t0SEkDw8Tt7mFEuUFhRiKrR527333mvzZ4KKEZR75VDwIYF41VByO3XqVLZHRpEpfod0QqTat29fZxxg7xTBvBTesk3WGGuRp5AEFvoNN9yw5lF4qkaNGmVJtd8LiEIPgerXr59V+LxhZ4Q0oZwTkpZXKGK55h+VW4gn1x8e6g8PY2xdu3Y1zZo1M927d691fVD/7D+MGU42/GQKUtW2bVvrEQxqGDUIR8Q7FeeIg3JhV65+2T9ggLcQg4RreNvmzZtX48HzPj/Jmrj75s6da8P2XJgfa4inEfLr9azicUYG+vbta7p06RI4bTy1EC6/Ny0LRlT0w+OGN47w6zB5yPIM3SsEhIAQEAJCoKEikJlMUUKccBUUN3/+A4ownirC8bxt/vz5NqwIKyj3+RVhSAwKY3191IvimSqlgDs8owpQFElwCSFEqWTNmzZtWjM0RwrJTyEczdvwDiIrQZXyUGwhkyiBKMRFbknJFB5YwvtQvPFm4Y3DSwTBxqtHfpOXfKUhU6wDnqxSrRyhnkVaJ7wy5JZBGiHyVCjEi4RnyG/MSbompeaJV2zkyJE2dwpPE80fBhl2f97hzfJMFUkiNRYhIASEgBCoNgQykykUZBLJycvwW6/DQl9QnsnrQDkJIlMQBHID/Ip1JcFNmjOVZmyNzTOFPEyYMCF0zcHQ72EjnA/PEx4tf9lxCAShWJwFVnTPSVIyRX4MhQaCwjvDwvz8VTWjPFN4YDCA1JfRIs2eyfsePNEU9CCniSIPhPcSVkroo7/4RtI1iRor7xhC65BhjAGuEEuPHj1CPVNRfab5XTlTaVDTPUJACAgBISAE/oNAZjJFOAzECGuuX6HFq4AXyp/EzYPJ9yFPhipu3nwFQsGo0kcRB391QO6rRM4Uz0lazS+NQEWRKfpsSDlThI0R6kTYnt8biWLP4ciEP3kbxJvCE8iRK5zgfp8zZ44hp2rw4MF18snwmOLRoXIeMoanoT5DAZOSKYgQXim/QQGZwZMC0crqmYKoUnGTsNZSRSbSyHbQPUXLmXJjdAQKDyfhc45Y+eeQdE24nzBU3mlBYbiEOUPaWE/nWSWPjbxR9kiljoxIQqacFxnZg4QjPxi+1ISAEBACQkAINFYEMpMpgEOhpToVeS2EyrhqVZQZpjJVWDU8ilaQm0SpbBK9v/rqKxuzjxIBOQtSflF8KAFNyzvcxSsERSFTWav5JRXsmTNn2pwNiEreIV4oYoTr4Q2gGh8KPHkiKI8okSTqB5XiJpyPCmYUbiDfikbJcDwFKL9BFf3ILenZs6dp06aNGTduXCAxT4pNluuTkikUXPLIULYp0kEVPio4uhwfcp2ykim3d8EH7x95ea4aIUYS9vXEiROtVzAPzx8hv+QJUfSCVq5qfknXyYX2Ie9UT2S+QeQy6ZowDrzsLneUapzuXYhxAI/YFltsYfbdd9+aIbNHyBl97bXXzKBBg2yFRd6H/J3cK/KlyDHEC0l1xzxaEjKFXOAtZR/zfg8ylOUxJvUhBISAEBACQqBaEMiFTPGhp7w1Sghkp3nz5tZbQBUx/n9Y4z7IFJ4D7sMzAYnCiht2vk21e6awQkMww5r/XBuuizpniipxvXr1skQ0Thn3UsJZ7mp+JLsT6scZOm7NyQ2ihH6pc6RQJCEXFCzh/0eVRodoIX/kwlCa2l8JsZIb1H92EfsETxlK/IABA6yCTPOuvX9voFSjuKJgM39Kanfu3Nl6PiDA5N64hiePggouJ8rJxuzZs+197ElIkvMC4qEiVJd1wYjAsxhL//797TlVeZ3vVVTPFLjhGYUklCIISdakXbt2djlYnwULFtgCPRCrKVOmWC8rIYXkAnIunV82ec6MGTPsmjzxxBO2uin9QbxYE9YmT69VEjLFuJA1ysazx7K+byq5D/UsISAEhIAQEALlQCAXMlWOgdV3n4SJoYSTjwPJU6seBFyBEyrkQb781SSrZyYaqRAoLwIYagirXnrppWtVNAx7KgQKDzIl9iHpakJACAgBISAEGjsCIlMlJIADZrEe4/nBk6BWHQi4g4dZu6ACDtUxC41SCJQXAecZ5Zw3wkn91Vj9T3c5nvwvXjMOEFcTAkJACAgBIdDYERCZauwS0ADnT24R4ZSjR48OPAi4AU5ZUxICZUcA8kX4NnlvW221VdmfpwcIASEgBISAEKgGBESmqmGVNEYhIASEgBAQAkJACAgBISAECoeAyFThlkQDEgJCQAgIASEgBISAEBACQqAaEBCZqoZV0hiFgBAQAkJACAgBISAEhIAQKBwCIlOFWxINSAgIASEgBISAEBACQkAICIFqQEBkqhpWSWMUAkJACAgBISAEhIAQEAJCoHAIiEwVbkk0ICEgBISAEBACQkAICAEhIASqAQGRqWpYJY1RCAgBISAEhIAQEAJCQAgIgcIhIDJVuCXRgISAEBACQkAICAEhIASEgBCoBgREpqphlTRGISAEhIAQEAJCQAgIASEgBAqHQC5k6u+//zavv/66ueiii8xjjz1mmjdvbo444ghz+OGHm2WWWabOpM8991xzxhlnhIJxxx13mAMOOKDewZo2bZo58sgjzYABA8xxxx1XcjzXX3+9Oeyww0znzp3Nv/71L9OuXbvI8b/zzjvmmGOOMVdeeWWs670dfvHFF2b//fc3Tz/9tCmFV5pxRQ48wwV//fWXeeqpp6ysPPnkk2bdddc1Q4YMseu9xBJL1OoZfPbZZx/z5ptvBj5xp512Mnfeeadp0aJFhhFlu3XGjBlmr732Mu+9917Jdcj2lGLeXdS5F03m3epl2e/FlIDyjSqpbN1///3m1FNPNRdeeKHp3bu3WWihhco3OPUsBISAEBACQsCDQC5k6plnnjH33XefOeWUU0yrVq3MH3/8YRVm/p1++ulmqaWWqgX6NddcY7p06WK6du1aZzFQhBZddFEzcODAel2ob775xhKdk08+2Sr8Ue2hhx4yvXr1Mttvv7256667zIorrhh1i8lDuYJM0MLIZ5pxRQ485QWQ7nHjxlniATlddtllzS+//GJQhL7++mtz9NFH27V37ZNPPjFjxowxJ510kmnSpEmtp3722Wd2bUaMGGFWWmmllCPKftvHH39sSe3zzz9v2Adbbrll9k6rpIeizr1IMu9dyjz2e5WIRuZhppEt3hd8g84//3yz2mqrZR6DOhACQkAICAEhEAeBzGTqp59+sh8wPFFebwyK8+WXX27WW289s91228UZi5k/f771UtAXZKs+G4rPWWedZa6++upYno8XX3zR7LjjjtYqClkM8siVYz5RZKq+xhU0188//9xiirKzwgor1Fzy66+/mrPPPtsSwo4dO8aCafLkyWbixInWw7X44ovHuqccF3333XfmoIMOMu+++641KCDvjaUVde5FkvnGIgt5zzONbGGYOfPMM60hLk5kQN5jVn9CQAgIASHQOBHITKa+/fZbG16BguwPt0LR//TTT82JJ54YC91Zs2aZiy++2JKw5ZdfPtY95booKZlyIWnbbrttRRX8KDJVX+MKWhfGcvPNN1vitOSSS9a6hNDPddZZx+y5556RS/rnn3/aPtZff32zxx57RF5fzgtQ4PCyvfXWW9Yj2Zgs4kWde5Fkvpyy15D7TiNbIlMNWSI0NyEgBIRAcRHITKbwTJH/dNRRR5k11lij1kxvvPFGs+aaa8b2TKFo472AnIXFvH/55Zc2HIx8m+uuu87svffeZUE3KZkiTI1wL8K8CG0Ma3hhTjjhBHPVVVfVXPLss8+azTbbrOQ9jzzyiBk9erSdNwr7gQceaI499ljz+OOP2/vCwvzijss9nPUE/1tuucV6kXjGIosskgvGrC2eJPr1kmXI0QUXXGCJURzPFP3gwTznnHPq3QKNBxZihzckLH+LMEbyqsjxAlfyCjEYQMB22203i/emm25aR+aRlRdeeME88MADhtA1Qp/w8g4fPtxA2hdeeGG7Lq5/QkzZh5deeqkh5AnDxNZbb21OO+00c/fdd9u9Av7eEFSeQf/kmjAeGs8g35Gx+fPYvIIQZ+7+68knJA9xhx12sHsgTjhsUuGLK/OMn9wcxvHEE09Yzzhz/+c//2leeeUV06NHj0APOf3zrrr11lttqC5ekF133dWuL6Ge7H8Xlppmv5NXSD9uv+PlZm33228/Gzo9ePDgOqSdfXvbbbfZfE3u3WCDDex+6tatm+H9Qkis34P70Ucf2XxN1h/ZQgYPPvhg069fvzqh2T///LPdc7zTafS50UYb1cgORrPjjz/eXuM1lDis7rnnHptXu9VWW9m9wDvsgw8+sO+XoJZUtuhDZCrpTtH1QkAICAEhkAcCmckUg0ChQKk/5JBDaj6kfCgpjsDH2ZsHEzZoPoRDhw61JKEUsXjuuefM5ptvbrs59NBDzWWXXVbHy5EHMMzpvPPOs8pGHC8Zygw5VigwKFZxmrO+EiYWNmdHVtdee21L1sgz+u2336zyjmKOMoqHJoxMJR2Xt+hDOQo8UNQDRZ/CEk4uXnrpJUNBjd133z1W4jgyQNGNUaNGlWXt46yd9xoUa8L88K6FhRziwUXZXG655azRwe2Vl19+2YYmXXLJJXVCBAkxJReLsNdNNtnELLbYYoZcMUgkHjzCSl2jf2QATJFZSBDySH4aRAnyQj7i77//bj1pGCvIbYR4YaCA4KDgQpz579tvv92uCc/y5zwmnbu73sk743AKeam9nnQd3PVxZf6NN96wawb56dSpk8WOex988EFLQCHH/vHNmTPHeuEhNOR8st5uP/J31jbsnRRnvzOHCRMm2DEQPr366qtb0kzYG3IGKWZc3jA2no8MrbLKKvYdwfsKQvb+++/bv/PO8I9p6tSplkBimHKEFgLz3//935bEkafYsmXLwCUAs/bt21tCxbsOws4YIWWQON5VtB9++MHKIKR8l112sXKEzEGqwJf3eCnDU5x95R2gyFTaHaP7hIAQEAJCIAsCuZApPpB85FFwf/zxR7PWWmtZxQRlAM9UnIYSj4UfRdCbT+O/txKeKZQKPEDk5ZRSkOPMq9Q1cZQrFH+aLkQAACAASURBVBQUZZRvv7cOazBEjByBvKofltMzBRbMmZyySZMmGaz2yArhoRCNMOXNiyFr45TWvOacdR3j3M8aYihgvfAweNcSjwK/R1WMdM+BTLriLl4yhVcKL3GHDh3sn5Fd9hJVJnke90GSnGI9ffp0M3bsWOut8hMmcEZp5768cKbPSnim4qwH10CmUNj984ecoOT37du3lmcKQkPIMkYfh7H3WYQpgycE1F8wxck+a1zKeOLIFBUseZbXEDVv3jzrleSfN5yU8fLuxKuGx9LbXn31VYNXyPse470BeYdoBRmK8NaR/4fHNMgzSV+QdDyfpYq/QKbwwuOt8uOFpxWyF1fm46ypC/9t27atxdh5buPcq2uEgBAQAkJACKRFIDOZQkHCQ9K6dWuz88471yiJ33//vf2AY/nEuhrVIA0oN3zg8wori3pm0O8o+JBAvGoouVisy9WiyBS/o9RBpLAE+1u1EQtIN9X3sFJvuOGGNdPBU4WXKUy5884bhRblDEUzTkhgudYuab9RuYV4cv1Wen+IFM/EG9KsWTPTvXv3WtcH9c/+w5jhyJCfTEGqUDzxCAY1Qr8IR4QcVKqgSlJcs15PaOMVV1xRE+a38cYb2/cYBgr/nPEGP/zwwxaPNO+oqP3u5sK+xnOE0QGjDuPgyAXCNPkXRHDoGyKHUcuF+XEPXk3C8bzkHSJDqCye/aBGSB+yiPctqJADcgVpi+NVnDt3rrnhhhssocMjhfziZYX4Ow9W1jX03s8+IkwTwwzPxROsJgSEgBAQAkKgnAhkJlOUECdkgw+sP/8ByzaeqrCPtpsYBAblmHAQwpHquxXFM1VKAXcYRRWgqG8svc/H+4iCg6w0bdq05idHCsnZiFp/wuIIE0PJryYFPymZwgOLIozyiZUd5RCFGKUZrx4kwEu+0pCpqPPeWKByhHoWSSb9YyE8DhmjhD/vNe87LcgjmGQucclUUJ8YIqheiacJb0+p0Et3P54h8sHwCvXp06eGUEW9M6LGmYRMBc0FIjdy5EibO0VOV15Nnqm8kFQ/QkAICAEhkASBzGQKBZkPNnkZfuU2rvKBBZzQFfI38HAVoSXNmUoz5iilpaF5ppAH8kFQxvyW/SgFz+GLNwUiFkXQ06xHOe9JSqYgjIQpBYV3hoX5+atqRnmmCHGDLIR5psqJR9H7Rh6/+uqrWmFolfJMhWHjjA6U34+7Zhi7IF8UfXGhgZX0TIXNhfcroYa88/PyHilnqui7SuMTAkJACDRMBDKTKeL4yXMi9MpPpsg5okJWVPlqPu5TpkyJVVK8EjlTLHXSan5pxCOKTNFnQ8qZgjQT7kPOiZ9ModhzODIhQGENWaPK3LBhwyLPIUOJxKND5TwqBeL5TBOalWZdg+5JSqYgQnil/J46ZIZcFYhWVs8U1dyozkZYaxxPR1YsipQztWDBAptn1L9//8BQNlfhzpvTA7mCmGD4CcqZ4l0HlshdkBzH2e+EsZL3RsirP3cU/DAmkGfoJVOQPMIBebY/r5L+qOTHP0em8siZihPmR34cHrWgnLsPP/zQvu+R5foiUxBm8hgJh2SsOpsq6w7X/UJACAiBxolAZjIFbMT3Ew9PSV0Sml3FJko2UxWqVDU8QjNQClEc41hbURooE0wrdzW/JIf2phGfOMpV1mp+Scc1c+ZMm3hP1bK8Q7xQBklsJ6yTsCMUeJLnKaZAMQQU1VKluEmMx5INeY9SwAiH6tmzp2nTpo0N2fKX7U+KS9brk5IpvE/kkaFwojxThQ+lGWWauZPrlJVMub0LPpBU8vJcNUKIK/ua0DLy3PIIqSTkl1wZcntoUUcCZMW81P1u70EoKX7CkQZu7uwBQvzwnPjzesCEHCvICXl/kFr2KH9HNjm0m4IfQRVM4+x3V5WxefPm1miAok9f7B3yOG+66Sb7fH/4Ie9OiN+AAQOsdx9SxT5Dhrjfb8BgvKw7e4694ao78h6nSAlezlLV/OKQKciKy5t1VQZZE0gnhHSLLbYw++67b27LnMQzxXuHMbCfWDdX4TK3wagjISAEhIAQaDQI5EKm+NBT3hqlC7KDIkDyMkoF/79UcxWyUAxRfKNatXumsMRCMMMaeUMc/uqtgohSVOqcKayrvXr1MmPGjIlVxr0UxuWu5kdOCqF+KITICt4ocoMooe8/yNc/TqzHFGVAIY9qhMkhf+RWUZ457NyyqH7y+N2dA+X6Yp/gKUP2UX7Hjx9vf/KuvVOe8aq5s8Xw8A4aNMiQ1E9ZaWdRR/kn/8Q1FG4KKLicKCcbs2fPtvexJyFJznsCoSBUl3XBI4sHg7HgtaHsddS6xMWoaJ4p8IFwQ9IpRQ6BhYhQ7htP+6qrrho4NT9e7pwpVyjGK2tJ9zsygVeKIgocN/Hoo4/WFJTAyMF71f9OdZ4pCA4FKMipo4Q+BSKQF4hWELlLcs4U5AsDCLIX1MASgun1/rJf8QCCKcSK6AN3lhel+CGwee7LJGQKfPCYYaSgCm2cIklx5VzXCQEhIASEQONCIBcy1RAhI0wMJRwFAYVfrXoQQGFDWaOylxSl6lk3jVQIZEGAs944NgFvbqtWrUp2RQg657QVwdiSZc66VwgIASEgBOofAZGpEmvAAbMo5Xh+8CSoVQcC7uBh1i6ogEN1zEKjFAJCIC4CeH8JTaQQEqGWpTxerpAH4cWENK688spxH6PrhIAQEAJCQAjUQUBkSkLR4BAg7InwqtGjR8c6CLjBAaAJCQEhEIoAoYfkilGW3X+AtmATAkJACAgBIZAUAZGppIjpeiEgBISAEBACQkAICAEhIASEgDFGZEpiIASEgBAQAkJACAgBISAEhIAQSIGAyFQK0HSLEBACQkAICAEhIASEgBAQAkJAZEoyIASEgBAQAkJACAgBISAEhIAQSIGAyFQK0HSLEBACQkAICAEhIASEgBAQAkJAZEoyIASEgBAQAkJACAgBISAEhIAQSIGAyFQK0HSLEBACQkAICAEhIASEgBAQAkJAZEoyIASEgBAQAkJACAgBISAEhIAQSIGAyFQK0HSLEBACQkAICAEhIASEgBAQAkIgFzL1999/m9dff91cdNFF5rHHHjPNmzc3RxxxhDn88MPNMsssE4jyX3/9ZZ566il7z5NPPmnWXXddM2TIEHPAAQeYJZZYohArM23aNHPkkUeaAQMGmOOOO67kmK6//npz2GGHmc6dO5t//etfpl27dpFzeOedd8wxxxxjrrzyyljXezv84osvzP7772+efvppc8cdd1jcglqacUUOPMMFadb966+/NldccYWd59y5c02fPn3MaaedZtZee+0MI8nn1hkzZpi99trLvPfeeyXXIZ+nFauXos69aDIftGo333yzOeSQQ8xOO+1k7rzzTtOiRYuSixt3v5dDQpKOtRxjKEefn332mX1vTpkyxZxzzjnm9NNPL/mYDz74wF6//fbbm+HDh5ulllqqHMNSn0JACAgBIVBlCORCpp555hlz3333mVNOOcW0atXK/PHHH5Yo8Y8PlP+jA/kaN26cVUAhKcsuu6z55ZdfzP33329QnI8++miz6KKL1iuU33zzjSU6J598siV6Ue2hhx4yvXr1sh/au+66y6y44opRt5gsZMp1jiJGCyNTacYVOfCUF6RZ9y+//NKceuqpVvHcZJNNzEILLWQ+/PBDM2rUKEt0O3TokHI0+dz28ccfW1L7/PPPG/bBlltumU/HVdBLUeeeROZffPFFu24nnXRSxRH/9ttvrWyff/75kWQq7n4v1yTSjLVcY8mr33nz5tn3Ct+usWPHmn79+kV2/euvv5qzzz7bdO3a1ey+++6R1+sCISAEhIAQaPgIZCZTP/30kyVReKK83hgU58svv9yst956ZrvttquF5Oeff27OOussq0SssMIKNb+5DxXEoGPHjvWKPkSHMV599dWxFB2Ush133NH07t3bXHPNNaEeubwnFUWm6mtcQfNMs+5YxZs0aVKHLL788stm0qRJVvYWWWSRvGGN3d93331nDjroIPPuu+9apQx5byytqHNPIvPPPfdcjdGn0uuWhqBE7fdyzSHNWMs1lrz6XbBggRk2bJi59tprzeOPP2522GGHWF3X1xrEGpwuEgJCQAgIgYojkJlMlfrI8tH59NNPzYknnlhrYhAVlGQsfEsuuWSt384991yzzjrrmD333LPiYHgfmJRMcf0+++xjtt12Wxu6uPjii1dk/FEf9voaV9Dk06w78gCmm222WR0ZuvDCC81ll11mll9++YpgHfQQPKp4V9966y3rkVxttdXqbSyVfnBR555E5kWm4klNQyRTzJz3y+jRo83DDz9sunTpEguMqHdurE50kRAQAkJACDQYBDKTKTxTZ5xxhjnqqKPMGmusUQuYG2+80ay55pqBnikIB54fryL8559/mgsuuMDsscceoZ4pwr4IAyTP6rrrrjN77713WRYjKZkiPJFwL8K8SsXe43074YQTzFVXXVUz7meffbYOWfBOinseeeQR+9Fn3ijsBx54oDn22GOtRZUWFuYXd1zueawnoUe33HKLXR+ekZfnB89U0nWHdCMjyIS3ka8DJoRn5TW+NIKEBxajAN6QsNwXwlfJqyI/BlzJK8RrCwHbbbfdLN6bbrqpDWH0r/sLL7xgHnjgAUPoGmF1eHnJ14BgLrzwwvZy1z8hpuzDSy+91HzyySfm4osvNltvvbXNL7v77rvtXgF/bwgqskX/EFPGQ+MZ5DsytlL5i3Hm7p0P15NPSHgmXgD2QJxw2KTrEkfmHWZhfROyO2bMmDpEnf1xww032Bw+1oOcUPAiJHirrbaqWRNvvx999JHNiwRn7xqSX8m7wh/ml2S///jjj3bNCVOj3XTTTWbgwIH2//vfNSNGjKhj2IqLrSNT5513npk1a5YZOXKk3X+8i9ibGBRWXXXVWt25XFruefDBB+1vRC/079/fjjFo7RlzHJl3D3LE+c0336zJPyMvk/3F92HllVc2l1xyiY0a8O8v+rjttttsyHDcPFfuEZmKKzW6TggIASHQOBDITKaAiQ8aSj3x587TRLIuxREOPvjgwPwnijug8OHNcflRL730kiHRmlj0oA8fz8KSvPnmm9vVOfTQQ61nwu/dymPpmBNKAEpQHM8HShYKFYrFrrvuGmsIzrJPmJjf8+I6cGSVYguQNfLLfvvtN6u8o5ijkKy//vqhZCrpuIKUk6jk+FiT/b+Lkq4744dEQgSc1wfcUHTxXrZs2TLJ48tyLYSPMD+s3GEeSZTR448/3iy33HLW6OD2CuGKZ555plX4/CGChJiSi0UILfliiy22mCFpnmR55o6C6Br9Q6jZS8gsJAh5JC8RogR5oTDD77//bhVf9he5jRAvDBQQHPCFmPLft99+u92LPKtUon2cubsxOnlnHLQoI0LaxUoi80k8U/QLeYc0sGdXWmklO0TIEnP6xz/+YY0P3nzPqVOnWoLD2nfq1MniDr4o8V999ZVh3SAmbo+l2e/cw5q6vEIvbhAanv/zzz9beQh7r0ZhzTjJHyVHEbmgr9atW9t+KeAAwYSoe3MYyUXj/Tx06FCz8cYb2/c0Y+V65Aa59BfqSSLz3jG78fXo0cManBhf27ZtLb6PPvqo3S9LL710nWlyLWOBePFujdNEpuKgpGuEgBAQAo0HgVzIFEoZlm+qrWEpXWuttaxCQT4LnqmghmJFbhF5L1gjuQeFAqWjlIJcCc8UCggf2cmTJ5dUkLOKSRwyhTUbhQBFya8IEUKJUoeVN8wzlXSM5fRMMZY06/7aa68ZrOp4tlBgkS08czvvvHOgJyDpnCtxPWvImFmv/fbbr9Zaoljze1TFSDfOIALA/Xgo8BI7hRZyR04iVSaRHe6DJDkDxPTp061HAyU4qEgMSiP35SVblfJMJVnPJGRq/Pjx1itD2LK/QI4jpt26daspQkJOGeuBJxEPSRDJwUPm9Wim3e+8r/AUsZZebyJ7BgIIKV599dWTQFPrWkfWt9lmG5tn5J//nDlzrIcHbyjGJ+YOThCpoCIxXI9BJCjUO2iQUevk9hdkavDgwWWtCAvWeLIwRIRVq00NtG4UAkJACAiBqkMgM5lCQcJDgpUS5dYp/N9//70lIoTk+T/iKB4ox7vssovZcMMNa0DDU8UHGUt9HG9QOdCG2EEC8aqh5GJNLleLIlP8ThgQRKp9+/Z1hgH2hAjh6chL4S3XXOk3zboTzocSi2LmlETCeAghhVgRjlUNLSq3EE+uPzyUcDWs5vfcc489eoBGFbFmzZqZ7t2717o+qH/2H8YMJxt+MgWpwnofVpWMkDTCpVDEG6rSGKWkO9nCG8z69O3bNzS35tVXX7WyyvsLDx99o3hzX5BHyB9KnGW/h3mnkB+q1jlvZNq94sg6ob9Bxz748QEL5DbMW8v1ECkiE7we2SQy751LJXO6WCfWmPkRvrjBBhukhVX3CQEhIASEQANAIDOZooQ4ORl8NP0x8Fhc8VQRjudtKBGEhXBP06ZNa35y5ID8kbiVlcqxBkXxTMVREKop5CTpujt5IA/NX3Kcvsi5IVSoGhT9pGQKDyzhfZAnvFl4a1HIUeTw5pLf5CVfacgU+w/PSakW9xykcuzDSvQZl0z5QxTDxuYNPY7am/41y7rfIW7k4xGejOEBrxReJGdwyYJnnLF5i8V4w7FLPdcb6plU5uuLTMkzlUWSdK8QEAJCoOEhkJlMlVJqwxQV/j5hwgRLpvzFA6IUkEotQdKcqTTjamyeqaTrDj54CTk02Z9PFEe5S7Mm5bonKZkiB4cCE0HhnWFhfv4zi6I8U3gtMIA05vNy4pIpVxyHMLK4Vd8q6ZlCbn/44QcbJk2Y20YbbWTDByEoWb1S9J3UM0U4JGcJQtbjVjZNKvP1RaaK8o0q17tK/QoBISAEhEAyBDKTKUJISHgfMmRIHQ8BOUfz58+vU4mN8CFCJIin95MpFDwOySXhPqhVImeK5yat5pcM9v9cHUWmuCZtDkWa8XBPOXOm0qw7xScozuEnUxRiILyUyohBFefwmOLRwVJPhUjCBOuz6l9SMgURwivl99AiM+SlQLSyeqYonEC4JIS1VJGJtLLkv6+oOVNhhh3OIUK2XIgeOWYcRM57K86h4pXMmXJYUwiI9y7h1cgHaxsUlpd0TePkTFHEgb2GLBEuTf4WYZFxD9ZOKvPVQKa8BX3IKa6GcOyksqHrhYAQEAKNHYHMZAoAqVhFTgcnyJPrRG4M/01yNcnP/vwnlCoOOOWD26dPH/vxJYYeRQWFJUxB5lmEWFACmlbuan5JDu1NI0hxyFSa6l5pxuLumTlzplWASBDPO8QrzbpDnqnwhWyR34NiC5HCik2+VJiXgJyNnj17mjZt2lgLub9sfxaM0tyblEzh1SB/EIWU4ixU4aOCI3lOVAQEi6xkyu1d8KEUOnl5zouAkYR9PXHiRJvfmEcopb+Md7mq+SVZH/I0qWRIOJyrEgrpx/uADFEJz4UvI7/33nuvoSDKoEGDbPVDSBV/nzt3rs2Xoj88hFRedPiWu5qfd77OO0XlOsadlxEhTTU/9i65RexDsEVuabzr3377bVtenuqHzlCSVOargUxxpAHvKb5z7piHJPKpa4WAEBACQqD4CORCplAmKGuO0gXZad68uQ01oYoY/z+oUUQAizAfVO7BG0WOCKXUS5U6r3bPFF4FCGZYI1+Mw1+9VRCjzp2hSlzYuThJRbCcninGkmbdKcVM5SwUWZRWFBPCmZCZsAbZQv4ooJClJHRS/IKu959p5M77wXNBCCNKOM279uwpiqBg6Xdni1F2HyUeDFBOOaeIqmIQYM6xcs2dNeRyopxszJ49297HnoQkOe8vHiryz9iPWNIhCYyF84A4pyqvoweK6JkCszfeeMPijBeThicUrya4BVU5pCgKeD3xxBP23Cg8P1tssYXFC9z8XqtS50zxHHLgMC65g8qz7ne8U+wPSHLHjh0zizAkgHBTjCtUg+RMJ0i+/8y7oHOjeJ9QyAc5pVQ6pBy56927tzXaUE7etSQyD+aEXpIzGXauH3JOgQh/vmVWQOKG+TE+xoYxCNyqpVhOVnx0vxAQAkKgsSGQC5lqiKARJoYSztkqpZT2hjj3ap8ToaV4G6iQR2hNlpLQ1Y6Fxt/4ECA0GILoqgo2PgTKN2MXKUDuHIc1l2p40Anrg2Ri3KmvCrXlQ0M9CwEhIASEAAiITJWQAw6YRSnH80O4jFp1IODyFFi7oAIO1TELjVIIJEcAbychi/zLwyuVfAQN9w6MM5Ajwsw5Oywqz5CwRbzJeKW8B2w3XIQ0MyEgBIRA40RAZKpxrnuDnjW5RYRTUryi1AHQDRoETa5RIkDI6CuvvGLI94xTJKNRglShSRPaSN4h76Io4lWhIekxQkAICAEhUAYERKbKAKq6FAJCQAhUAgF/UQ/3zHLlC1ViTnqGEBACQkAICIFqQkBkqppWS2MVAkJACAgBISAEhIAQEAJCoDAIiEwVZik0ECEgBISAEBACQkAICAEhIASqCQGRqWpaLY1VCAgBISAEhIAQEAJCQAgIgcIgIDJVmKXQQISAEBACQkAICAEhIASEgBCoJgREpqpptTRWISAEhIAQEAJCQAgIASEgBAqDgMhUYZZCAxECQkAICAEhIASEgBAQAkKgmhAQmaqm1dJYhYAQEAJCQAgIASEgBISAECgMAiJThVkKDUQICAEhIASEgBAQAkJACAiBakIgFzL1999/m9dff91cdNFF5rHHHjMcGHnEEUeYww8/3CyzzDKheLzxxhvmggsusPe0bt3aXn/IIYcU5rT4adOmmSOPPNIMGDDAHHfccSXX9frrrzeHHXaY6dy5s+Hk+3bt2kXKwTvvvGOOOeYYc+WVV8a63tvhF198Yfbff3/z9NNPmzvuuMMccMABgc9LM67IgWe44K+//jJPPfWUlZUnn3zSrLvuumbIkCF2/EsssURgz7/88ou58cYbzbXXXms+++wzs/POO5vhw4ebDTbYwCy00EIZRpP91hkzZpi99trLvPfeeyXXIfuTitdDUeceJfPfffed3dPjx4+v6Jpl2e+VWH3vAcDnnHOOOf3003N7bBHnzruE986UKVNMnPl+8MEH9vrtt9/evn+WWmqp3PBRR0JACAgBIVC9CORCpp555hlz3333mVNOOcW0atXK/PHHH1Zh5h8f5KCPzpw5c8zxxx9vjj32WLPtttsalOy3337bftgGDhxY7x+qb775xhKdk08+2Sr8Ue2hhx4yvXr1sh/au+66y6y44opRt5g8FIw777zTPieMTKUZV+TAU14A6R43bpwlHpDTZZdd1kCU7r//fvP111+bo48+2iy66KK1ekeWLr30UsN6DB061Ky00kpm7ty55tFHHzVrrrmm2WyzzVKOJp/bPv74Y0tqn3/+ecM+2HLLLfPpuAp6Kerc48p81N7Jewny2O95jymov+eee67m3Z3X84o493nz5lnjHd+usWPHmn79+kVO99dffzVnn3226dq1q9l9990jr9cFQkAICAEh0PARyEymfvrpJ0ui8ER5vTEozpdffrlZb731zHbbbVcLyd9++82ceeaZZptttjE77rhjrd/46EKqICb12RjHWWedZa6++mrTokWLyKG8+OKLdi69e/c211xzTUmPXGRnCS6IUgjra1xBU/j8888tpueff75ZYYUVai5xCgqEsGPHjrVuxXuJR2rkyJGWfLmGfKEA9ejRw/zjH/9IgFi+l+LlOOigg8y7775rlTLkvbG0os49rsxH7Z3Gso7+eZaDTBURywULFphhw4bZ98vjjz9udthhh1jDlNzEgkkXCQEhIAQaDQKZydS3335rTj31VKsg+0kHH51PP/3UnHjiibUA5R48Ptzj9+Dw22WXXWZ/r88wiqRkiuv32Wcf62UjhG3xxReviBBFfdjra1xBk2csN998s7XsLrnkkrUuOffcc80666xj9txzz1p/Z35//vlnoNUYLxdEyk/WKwL8/z0Ezxpetrfeest6JFdbbbVKPr5en1XUuceV+ai9U6/g1uPDGwuZAmLeO6NHjzYPP/yw6dKlSyzUJTexYNJFQkAICIFGg0BmMoVn6owzzjBHHXWUWWONNWoBR54LoVh+ZTeKTBHORQx7kGL65Zdf2nAw8m2uu+46s/fee5dlsZKSKcLUCPcizKtUrgFemBNOOMFcddVVNeN+9tlnS4arcc8jjzxiP/rMG1wOPPBAGyKJRZUWFuYXd1xuMKwn5PiWW26xXiSescgii+SCMZ4piCb9Lr/88jV9QpbIndtjjz3qeKZKkSl+++qrryLz2XIZfEgneMggh3hDGE+QF5MwRvKqdtppJ4srOYJ4bSFgu+22m8V70003rZP/xbq/8MIL5oEHHjCErhFWx14iXwPSvvDCC9tRuf4JMWUfEhb5ySefmIsvvthsvfXW5rTTTjN333233Svg7zVg8Az6v/DCC+14aDyD/EXGFpbHxnVx5u6FjevJJyQPES8AeyBOOGzS9Ysr86wXY4LEe/M9IceDBw+uQ/gZR9w1cWNOut9//vlnm0PIu5PGu2GjjTaqWSOMU4RHc43fIJEEJ8KqCUslX5N3ymKLLWYNGUQZ8AwXou36TDOupHPnWbx/brjhBnPFFVdYeSfnFnkk5HqrrbaqkXk3Lkec33zzTbu/WFPmxv7i+7DyyiubSy65xEYNBOVX3nbbbWbUqFGx81x5rshUEknTtUJACAiBho9AZjIFRHzQUOqJP3cfeJJ1KY5w8MEH18mDIcyPjzYfOG+YH4oN9/DhJIQryFKI1XTzzTe3K3PooYdaL1YWpSJsiZnTeeedZ5UNr+Ifdj1KAOOGEOy6666xJMdZ9gkTC8v9cWR17bXXtmSNUDfwQ3lHMUcZXX/99UPJVNJxBSknccIcY03YGENRDxR9vHguP+qlY1/INgAAGn1JREFUl14yFNQgB8Gv8Lz66quWRKLweMP85s+fb0466STTpEmTinoCg+aJt40wP6zcYR5JDAgowcstt5w1Ori98vLLL9uQVxQ+f4ggIabkYhFCu8kmm1iFl6R5DA0ovt69Q/8QajBFZiFByCP5aRAlyAuFGX7//XdLPsHZ5aNhoIDgQNIhzvz37bffbteEZ5XyEMeZu8PMyTvjcEShHDlvcWUepRiSyv5x5IR78TBCLEeMGFHn3ZJkTfyyEme/e+9Bntq3b28JFe8UiDEEGvJL8RXeCWka6w5ZoeAEpBnvLn8jjxXZIe8VWQszCqUZV5y5gz17etVVV7Whs+RH0j766CMru4wT444/r5JrnIGOsF/IIbLftm1b+3fyK9kvSy+9dB24uBYZhnh53y+lcBWZSiN1ukcICAEh0HARyIVM8SHG8k1VOT7Qa621lv3gQZjwTAW16dOn2w8jXgosjyhwWGOnTp1qFTq8XUGKViU8U5A6PrKTJ08uqSBnFYs4CgaKEwoByrefaGBBRumgYEeYZyrpGMvpmWIszJmcskmTJlkrP7ICWYNotGzZss5wuQbPDXMnXBRii0UdgrDKKqtYZReiVQ5CnRS7UtezhngTWa/99tuv1lpiHef3qIqRrv+gMCzuxyvFvunQoYO9FKWX3DSqTIIf90GSnAGCPYjRAq+MnzCxB1AauS8v2aqUZyruujE/yCoePO/88cIgj+yruKFfcULj4ux3P5mCDDM+Ryzizq3UdRB4CAYeSz8x+eGHH6znn31ZikwlHVecuVNdcdasWXafhxWi6datW2CRF7e/IFN4FUt5VLNiyLcBDyse4FLVarM+R/cLASEgBIRAdSCQmUyhIOEhobQ5Jaudwv/9999bZY4P8+qrrx6IRlBpdCoqkUtFWBQVkyrdUN4hgXjIUHI7depUtiFEKRj8jkIDkcJC7W9gD1Z4OvJSeMs2WWOs9Rtr/y677GI23HDDmkfhqYIQ4aEJ8gKGlUYnnAcCDxnAQ1XkFpVbiCfXr7wSrobV/J577rFHD9DYE82aNTPdu3evdX1Q/+w/jBlONvxkClKF9T6sKhlhVoRL4Z1qiEpjmIeh1L5MsiZ+eYza7/7rWT/COfP03rl3BuHIYZUnwSVIHt340owrau5425H/vn37hhJYvNQQLt4T/tDjUvsr7/cCc2EM7MsHH3zQegnVhIAQEAJCoPEikJlMUbIaCycfWH/+Ax9lPFWE48VthG9hXSfspr6S+YvimYqjIFRTyAkhhORDICtNmzatEQmn4JE3FLeiFjfjOZw5c2Zsj05cGSzHdUnJFB5YwvsgT3iz8NphqECRw6tHGJqXfKUhU6wDe61Uc3koeYZ6lgPfNH0mJVNJ16SIZMoRAbxuYWfhRXnZykGm/CGgYesZFtod512ZRkaC7pFnKi8k1Y8QEAJCoGEgkJlMoSCTSE7Yld96HfVRDoKQ0COsfSh55QzViFq+pDlTUf0F/R5lrW1oninkYcKECZZM+S3LSUkhHkQ8Jn369LEJ+kVvSckUOSLkxwSFd4aF+fmrakZ5pvB6YQBprOflJCVTSdekiGSqqJ4pV4SGML24oZVefCtJppK+q4r+btL4hIAQEAJCIBsCmckUBx+StIwnyU+m8BzgaaIoQ5yG5ReFkLM/XN6H/75K5EzxzKTV/OLML41y1ZBypggbIzSGao1+MoViz+HIFFqIau7w33//+9+2r6CEdDymeHSonEelQPIw8qpKGDW+oN+TkimIEF4pv6cOgk0ODUQrq2eKxH7yFAlrrcQxBEXMmWKt/CGyYUaOpGuSZr9770njAYojm3nkTCUNP4wyHDFuDGlUEQzb06XmVlQy5S3oQ0hyNYRjx5EhXSMEhIAQEAL/j0BmMkVXFI0gp4N8J3JeyI3hvynZTHUmfx4MuS4owuRZoQi762+99VabYxWUH+SGTIgFJaBp5a7ml+TQ3jRCFUfByFrNL+m4CJsjb4HKXnmHeKFMc7AtXiU8Sijw5EqgQKFIUTLe743kYFjkxYWZ8d/ICVXz8NqEeS/Jr+jZs6dp06aN4Twqf9n+pLhkvT4pmcL7RB4Z+WAUA6AKHxUcyXOiIiC5TlnJlNu74ENVN/adq0aIkYR9PXHiRJvnlkfOFCG/FMmg6AUt6kiArJhH3Z/UM5V0TYpKpvKo5lcOMsX74d577zWvvfaaGTRokA3z5vvA3+fOnWvzpcivxOBGtUFvKyqZolokB9DzvsNgFKcybJTc6nchIASEgBAoFgK5kCk+dpS3RumC7DRv3txWVKKKGP8/SKmgFDTleWlU88N7hfIbZSGvds8UXgUIZlgjb4jSzN4qiFHnTFEljg/2mDFjMn+sy13ND2JEqB9rj6zgjSI3iBL6QRX5IFlYqimpTqI3JaIpEY/iH3RujMOVkCzkjwIKlEkudW25t6Q7B8o9h32CpwxiOGDAAKsk0rxr744JwKvmzhZjj6BkolhyPEDnzp1tVTEIMAVbXLvppptsJTqXE+VkY/bs2fY+9iQkyXkB8VARqsu6YElHiWUs/fv3t+W486qUWBTPlB/3bbbZxu45KubhtaFAChjTMO7wroK0J1kTl4+UdL9DYFG83fP9ssmacqB5Vi9r1DlTyIlXHtOMK+ncmSsYz5gxw8rjE088Yc+aAsstttjCyiNjcp5oQgMJLw+rOoicEzIeVmgj7b6PG+bH+Bgb5fSposk+VBMCQkAICIGGh0AuZKrhwWIMYWIo4SguKPxq1YMAoaWcm0RFMkJrwqpJVs+MNFIhIASKgICLFCC3y38YvX98nAlHWB95iRh35JUqwgpqDEJACAiB/BEQmSqBKd4QlHI8P3gS1KoDAZenwNoFFXCojllolEJACBQJAYwzkCPCzIcPHx4ZRUFYKN5kvFLeA7aLNCeNRQgIASEgBLIjIDKVHUP1UDAEyC0ixGj06NGBBwEXbLgajhAQAg0QAUJwCY/0HwrdAKeqKQkBISAEGjUCIlONevk1eSEgBISAEBACQkAICAEhIATSIiAylRY53ScEhIAQEAJCQAgIASEgBIRAo0ZAZKpRL78mLwSEgBAQAkJACAgBISAEhEBaBESm0iKn+4SAEBACQkAICAEhIASEgBBo1AiITDXq5dfkhYAQEAJCQAgIASEgBISAEEiLgMhUWuR0nxAQAkJACAgBISAEhIAQEAKNGgGRqUa9/Jq8EBACQkAICAEhIASEgBAQAmkREJlKi5zuEwJCQAgIASEgBISAEBACQqBRIyAy1aiXX5MXAkJACAgBISAEhIAQEAJCIC0CuZGp//mf/zHnnnuuuf/++80yyyxjDjzwQHPssceaFVdcMXRs3HPmmWea++67zzRv3twcccQR5sgjjzRLLbVU2vnket+0adPseAYMGGCOO+64kn1ff/315rDDDjOdO3c2nHzfrl27yLG888475phjjjFXXnllrOu9HX7xxRdm//33N08//bS54447zAEHHBD4vDTjihx4xgveeOMNc8EFF5jHHnvMtG7d2hx++OHmkEMOCV33P/74w8oI97z11ltmt912M2eccYbZeOONM44k++0zZswwe+21l3nvvfdKrkP2JxWvh6LOvYgyX67V432L/O20007mzjvvNC1atCj5qCeffNJsv/32id5T5Rp71n6Tzt3/vM8++8y+N6dMmWLOOeccc/rpp5cc0gcffGCvB7/hw4fH+k5dfPHF9t3FO75r165Zp6z7hYAQEAJCoIAI5EKmvvzyS0ucUHL33HNPs9hii5mPP/7YPPLII2bfffc1LVu2rDN17hk5cqQZMmSIWXnllc3ff/9tleu3337bHH300WbRRRetV7i++eYbS3ROPvlks+6660aO5aGHHjK9evWyH9q77rqrJIl0nWUhU64PFChaGJlKM67IyWa4YM6cOeb444+38rLtttuav/76y645Cs3AgQPrKCjIxd13323+/PNPSx4XXnhhM3fuXDNixAjTv39/06FDhwyjyX4rcs64nn/+efPMM8+YLbfcMnunVdJDUeeeROZffPFFu24nnXRSoVDHIEPbZ599Isf17bffmlNPPdWcf/75kWSKzn755RdrxGK/xTH6RA6gHi9IOnfvUOfNm2eNOJCdsWPHmn79+kXO5NdffzVnn322JUa777575PVc8Nprr1kydcUVV5jlllsu1j26SAgIASEgBKoHgVzI1NVXX22WWGIJ+2FaaKGFamYPIYFQHXTQQWaRRRap+TuKMV4GrKmbbLJJzd9RnOmrW7duZqONNqpXFCE6Z511lh1PlLWXgaKU7bjjjqZ3797mmmuusd65SrQoMlVf4wqa+2+//WaVuG222cZi5W3gDamCkHrbrFmzrMWd+5Ax1z788EP79xNOOKHW3yuBufcZ3333nZXvd9991ypl6623XqWHUG/PK+rck8j8c889Z5566qlIr0SlQY7a197xJCUUIlP/QW/BggVm2LBh5tprrzWPP/642WGHHWItc5K1ocOk6xNrELpICAgBISAECoNAZjLFh5nwCIhU+/bta02M3whzGDx4sGnVqlXNb4RXXHbZZTZUy086UIQmTpxof/MSsEojlpRMcT1WZLwtF110kVl88cUrMuSoD3t9jSto8igVePqwoPvDP/kNmeB3b5gnIVtrrrlmHUUHRQgZOfjgg03Hjh0rgnXQQ5BxQkAJP8Qjudpqq9XbWCr94KLOPYnMi0xFhyNXWq6SPC8rUSE0ffTo0ebhhx82Xbp0ifXoqHeuv5OsY4w1KF0kBISAEBAC9YZA2cnUiSeeaMOxvB8qci0effRRG1rj9WSBAorQDTfcYPOvmjZtWgcYwgMJAyT2/7rrrjN77713WcBLSqa+/vprG+5FmFep2HvCRPCmXHXVVTXjfvbZZ81mm20WOg/uwcPHR595o7C7nDQsqrSwML+443IP/+mnn2zI0C233GI9c4Tj5UVqo8jU0KFDbe6CIyR4MCGmhI76iTrjRUa22mqreg2tw5tK2A9GgLCcFW9uB7gSznr55ZfX5H+B96abblpnL7DuL7zwgnnggQcMoWuE1W233XY2XwPSTsgjzfVPiOlRRx1lLr30UvPJJ59YQ8bWW29tTjvtNBsqyV4BTy+R5Rn0f+GFF9rx0HgGeWyE7Xq9gX4BjTN37z1cT/gaeYh4AdgDpXIq027sODLvMAt7Bh7SMWPGmOWXX77WJcyB9xchW+PHj7chp1F4saduu+02G0r20ksvWfneY489bBjx6quvXqt/1qxUyCHvvksuuaTWujhl/ZRTTjEPPvigfU/w/urTp481OKy//vq1nuH1TOEtJtya+wjPZt4YB/yhzT///LMNyb7xxhttX7yziB5wsvPpp5/a8F2uWXLJJa3sMY9DDz3UGkn4G+3mm2+2hjdaUJ4SYb+EXbp3HcY25Hq//fazHkQMc16DRdK5+9ebdRk1alTsPFfuF5lKuzN1nxAQAkKgYSKQmUwBCx/LpZdeuk6Y38yZM+3f+Mh748uxBk+YMMEqw35FPYrEcO/mm29uV8P/oc5ziRjHeeedZ2Pd/QpV0HNQmFCOUJJ23XXXWENxln3CxMLIFP2iEK299tqWrC277LIGBQjlHcUcZRRlKYxMJR2Xs+q/+eabsZPaY03WGDtuZIEQP2+YHwoqhTTAD4XTEW/w4XoKgASFzyE/eK3C5h53XFmvQ0EkzI/xhHkkUfpQNsmZWGONNey+QMF8+eWXbQgjCrJ/joSYkotFYRbCYVF28eqihEIwvRjSPziQa4jMQoLAk8IYECXIC16+33//3SrLGDEo7AHxwkABwUFJZT/y37fffruhyAnPKlUQJs7cHb5O3hmHU8hLGRHSrksSmU/imUJO7733XgNxQKl3uEAAWEe8G8irFy+whFiwNhtssEENYf7hhx8smeRdhkHA35Io7Kw9JBp5Yh3ZP4yJtWc8GCm8ODsyxXpPnz7dXtO2bVsrD7xXGBf9BY2LcSLnGDcgVLzrIOwQe4gVc+RdReNdgnxgbHBkys2T+VHQwW944rsAsWNMEE36JZyUfjAIcJ83zyvp3P04Y5yibwxzvFvjtCRrQ3/yTMVBVdcIASEgBKoXgVzI1EcffWSJDaQA6zfWbix+WH9RHvmbV+FFgUFZ81osHYRRZKoSnimUJj6ykydPLqkgZ132OGQKBYWPsT8fjWej1IE5ieR5EYpyeqYYM8ob3i68Xlj0WU+s3VOnTrWKPMTRKX5R+BSFTMWRA9YQbyLrhZXd65Flr/B7VMVI95wgAuCUSvBzRTnAZ4UVVrBVJnmef9+xFpBXvFV+wsQeQGnkvrxkq1KeqTjrUQrLsPvxSJEXhycxyGOHl5j3F0TEkVVyQ3v06BGYAwqhgqxyvd9DlURhZ+15ByA/fgLEvvqv//qvWqHHbl+x3yB6/rWHROPN5J9/XI5MQdS4d6WVVgqFOy2ZwpBDRIO3CBHFIvDI8s/vmUoy9ySyEXYt3wY8rBgi4uTG8k5lbfr27Wvfef5ojDzGpD6EgBAQAkKg/hDIhUwx/LDS6JTtpvy1typVFjJVbqggglhF8ZSg5Hbq1Klsj4wiC6Xy0RgUyin5R3g68lJ4yzZZT8dBpdGppMVcKPPsSghH4VNtZCqs4lqYlZ5wNazm99xzj3n99dctgmDTrFkz071791pW/SDrtx8f/77DmIFHIqwqGWGFhCOi8MdRGishO3k/I4lnCrx4H4QVKmC98LTg0YbExsGPdwykxr9/k5KpMNkKeteyr/BWQe7DPIOlZAO5Isw0yquYhkzxToMAUsQH0oLccdwEBjn++UlsKa9Pqe9MFjlynj32JV40vHFR7ZVXXrGVbXm/sZ9Khc9G9aXfhYAQEAJCoFgI5EamgqZFzgshHsTh+3OmyEfAausP/4jKmaoEfEXxTMUJD0midFUCu7TPmD9/vvVKkXPhzZlCOSWUKEhxK0LOVNz5llrLIDKFx47wPsgT3iyOF8CijSI3adIkm9/kDZFKQ6bAD8xLtbjnF8XFoWjXJSFTUSTCeQfxuhKKVopMOBzCiHSSfZ2UUMSp5lfq+VE4uLmlIVNB8kH4IUWJXn31VZtv6vWkJZ17HvInz1QeKKoPISAEhEDDQaCsZIrwP5Kb+fh6847I+yBPhFwOLLjehlWSBP2gfKpKwp40ZyrN2KI8Lw3VMxWEFSFnWHlR7r1WW/JryMfzW+5JiCfkh/DHaihHnpRMMW/yRYLCO8PC/PzeiSjPFF4vcu7inpeTRsaLfk8SMiXP1H9Ws9Jkimc6Lzx73Suv9UGmkhBdxh7HKFb0faLxCQEhIASEQDgCZSNTxIljOccr5Y/j58PI2R5UjPIecooFkhAZQkjCDj+tRM4UcEXlbuUhVFFkimc0tJypINxYU4gAZ774D+GFkN900032d68Xk/yVW2+91YYG+r2bPIMzzvDoQMzxgJKDkVdVwjRrn5RMobDilfKHlLnjBiBaWT1TYEu+mr9oQpr5xbmnqDlTYcVwKL8PsXc5LpXOmfrxxx9tLqq3gSGhyE2aNKn5c1JCESdniogCDBveIy3cA5OQqbDKrIQ3EgbplWEKTfBMDG1+IxvzhsyutdZaIlNxNpuuEQJCQAgIgYohkJlM8ZHD09S8eXOr1FJF6v3337cVxYgRJ7cjKOEWBXrEiBG2GAFhXdxHKAdV0Sj/600+9qJBiAWlcmnlruaX5NDeNCsWh0xlreaXdFxUYCRRes6cOblX82ON//3vf9scOtYX8kwuEKSINQ8qf458UcULxRZyxH3kpuDxpOS+n3y5+RIS1LNnT9OmTRszbtw4m1dWny0pmcJjQslmikOgQFKFj0prKJQUdSHXKSuZAg88weBDKXTwd9UISfjnN/Yk+zSPnCnIAcUWKHpBizoSoBLrRQl5KuBB5F2VUJR8vA/IEETelW931fwokMA8HNGgSiVlu6dMmWLXxF/ND3JA9UUOq0Z+3TsTgxIVGYOq5pFXiAEB7z15WuwVDrWG/BI+TfVH95w0ZIoxZanmFydnincXRJ19St4TBgCKbhDiTVl58PDLMB5oviWsB/c4vMhhZS0oSe8tp5907nnIlDxTeaCoPoSAEBACDQeBzGSKDzsluvEQcOYKuS2cb4Ly0KJFi5JIURoXCyjhXXxAqXhEyeEgT4PrqNo9U1HnyHDeEIe/UvLbtahzpkgkDzsXJ6molrOaH+QRJRCFiEZlK0rJQ3pKld9GkXTVH8kVCjs/xztXwuSoYkcBBUqE12cFLf+ZRpATPGVY4in7jmJJ8669KxePV82dLQZWgwYNsvsMxR9lk6piEGAS211D6aTCmcuJcrIxe/Zsex97DZJEuXUaHirKYeOhwSOLks1YUIIpe11qPyaRryJ6phi/tyAK/807DOIObkFVDst5zpTD07/+rBn75Z///KclIS4U1i9bbu3959m5NSc3EWOUkx32O4QdDy6l93lvu7Lk3rWFWLPvkL2ghqxx4Lbf+0vVP0gh/TMGd74WxgLeW16ZZz9A8pgjlRE5i5AzpyjwgIGHbwN9uJZ07k7ek8hs0LUiU1kR1P1CQAgIgYaFQGYy1bDg+P/ZECaGEo6C4D/AsqHOuaHMC4URbwNknWqSQeWdG8pcNQ8hIAQqh4CLFKDcPeQ2qkGKIY541zGilDIaRfWl34WAEBACQqCYCIhMlViXadOmWaUcCyqeBLXqQMAdPMzaBRVwqI5ZaJRCQAgUCQGMM4Qh4tmj+E0cYkQkAmeTEfbujnwo0pw0FiEgBISAEMiOgMhUdgzVQ8EQILcIJWb06NG2pLiaEBACQkAICAEhIASEgBAoBwIiU+VAVX0KASEgBISAEBACQkAICAEh0OAREJlq8EusCQoBISAEhIAQEAJCQAgIASFQDgREpsqBqvoUAkJACAgBISAEhIAQEAJCoMEjIDLV4JdYExQCQkAICAEhIASEgBAQAkKgHAiITJUDVfUpBISAEBACQkAICAEhIASEQINHQGSqwS+xJigEhIAQEAJCQAgIASEgBIRAORAQmSoHqupTCAgBISAEhIAQEAJCQAgIgQaPwP8CN0polRS+NjsAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ```category_index```\n",
    "![category%20index.png](attachment:category%20index.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}